CN117651857A - Light absorption remote sensing (PARS) imaging method - Google Patents
Light absorption remote sensing (PARS) imaging method Download PDFInfo
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- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Abstract
A method of visualizing details in a sample may include: generating a radiation signal and a non-radiation signal in the sample at an excitation location using an excitation beam, interrogating the sample with an interrogation beam directed toward the excitation location of the sample, and detecting light from the sample. The excitation beam may be focused below the surface of the sample. The interrogation beam may be focused below the surface of the sample. The detected light may include a portion of the interrogation beam returned from the sample. The detected light may be indicative of the generated radiation and non-radiation signals.
Description
Cross Reference to Related Applications
The present application claims priority from:
U.S. provisional patent application No. 63/187,789 filed on 5/12 of 2021
U.S. provisional patent application No. 63/241,170 filed on 7/9/2021
U.S. provisional patent application No. 63/315,215 filed on 1/3/2022
U.S. patent application Ser. No. 17/394,919 filed on 8/5 of 2021
This is a continuation of PCT/IB2021/055380 submitted at month 17 of 2021
PCT/IB2021/055380 claims 63/187,789 (supra) submitted at 5.12 of 2021, 63/040,866 (0007-01600) submitted at 16.6 of 2020, and part of the continuation-in-patent of 17/010,500 (0007-02000) submitted at 2.9 of 2020
The rights to 63/040,866 (0007-01600) filed on 16 th month 6 of 2020 were claimed in 17/010,500
PCT/IB2021/055380 submitted on month 17 of 2021
The interest of 63/187,789 (above) filed on day 5 and 12 of 2021, the interest of 63/040,866 (0007-01600) filed on day 6 and 16 of 2020, and the part of the continuation-in-patent of 17/010,500 (0007-02000) filed on day 9 and 2 of 2020
The rights to 63/040,866 (0007-01600) filed on 16 th month 6 of 2020 were claimed in 17/010,500
The above application is incorporated by reference in its entirety.
Technical Field
The present invention relates to the field of optical imaging, in particular to a light absorbing remote sensing (photoabsorption remote sensing, PARS) system for non-contact imaging of samples such as industrial materials or biological tissue in vivo, ex vivo or in vitro.
Background
Photoacoustic imaging can be divided into two main categories: photoacoustic tomography (Photoacoustic tomography, PAT) uses image formation based on reconstruction, while photoacoustic microscopy (photoacoustic microscopy, PAM) uses image formation based on focusing. Because conventional photoacoustic techniques require physical coupling to the sample, they are not suitable for various clinical applications such as ophthalmic imaging, intraoperative imaging, wound healing monitoring, and many endoscopic procedures.
Disclosure of Invention
Aspects disclosed herein may provide a method of visualizing details in a sample. The method may include: generating a radiation signal and a non-radiation signal in the sample at an excitation position using an excitation beam, interrogating (interrogating) the sample with the interrogation beam towards the excitation position of the sample, and detecting light from the sample.
The excitation beam may be focused below the surface of the sample. The interrogation beam may be focused below the surface of the sample. The detected light may comprise a portion of the interrogation beam returned from the sample. The detected light may be indicative of the generated radiation signal and the non-radiation signal.
The return portion of the interrogation beam may be indicative of the generated non-radiation signal. A portion of the detected light other than the return portion of the interrogation beam and the excitation beam may be indicative of the generated radiation signal.
The method may include: local optical scattering from the sample is detected.
Detecting light may include: the generated radiation signal and non-radiation signal are detected over time. The method may include: the evolution time of the detected generated radiation signal and non-radiation signal is determined.
Generating the radiated and non-radiated signals in the sample may occur at multiple regions in the sample. The method may include: a region belonging to the nucleus is determined or identified in the plurality of regions based on the determined evolution time.
The method may include: determining at least one of the following based on the determined evolution time: thermal diffusivity of the sample, conductivity of the sample, speed of sound in the sample, temperature of the sample, density of the sample, thermal capacity of the sample, acoustic impedance of the sample, tissue type of the sample, or molecular information of the sample.
The method may include: determining an average pre-excitation signal; determining an averaged post-excitation signal based on a predetermined portion of the detected signal over time; and determining an amplitude based on a difference between the determined average pre-excitation signal and the determined average post-excitation signal.
The method may include: based on the detected generated radiated PARS signal and non-radiated PARS signal, a function is used to determine a value. The value may be a ratio of the detected generated radiated PARS signal to the non-radiated PARS signal.
The method may include: redirecting a portion of the returned interrogation beam and detecting interaction with the sample.
The wavelength of the excitation beam may be configured such that the sample absorbs two or more photons simultaneously. The sum of the energies of two or more photons may be equal to a predetermined energy or absorption.
The wavelength of the excitation beam may be configured such that the sample absorbs two or more photons simultaneously. The wavelength may be equal to twice the predetermined wavelength. The predetermined wavelength may be a wavelength in the Ultraviolet (UV) range. The predetermined wavelength may be a wavelength in the UVC range.
The method may include: the detected generated radiation signals and non-radiation signals are clustered based on shape using a clustering algorithm to determine characteristics of the sample. The method may include: a cluster center is determined based on the cluster signal and an image is determined based on the cluster signal. The method may include determining one or more colors based on the cluster signal.
Interrogating a sample with an interrogating beam may include: the interrogation beam is moved over the sample over time to interrogate the sample over multiple regions.
The method may include: the non-modulated scattering caused by the spatially varying movement of the interrogating beam across the sample is estimated.
The method may include: a plurality of filtered instances of one of the generated signals are measured or stored. The plurality of filtered instances may include an unfiltered instance of the signal and a filtered instance of the signal.
The method may include: determining a first image based on the detected generated radiation signal, and determining a second image based on the detected generated non-radiation signal; comparing the first image with the second image; and determining one or more modifications to the final image of the sample based on the comparison.
The interrogation beam may comprise chirped pulses. The method may include: the individual wavelength components of the interrogating beam are spatially separated.
Multiple detectors may be used to detect light from the sample. At least one of the plurality of detectors may be sensitive to or configured to detect radiation relaxation. At least one of the plurality of detectors may be configured to detect fluorescence or autofluorescence and/or may be sensitive to fluorescence or autofluorescence.
The excitation beam may be focused in a smaller area than the area in which the detection beam is focused.
The method may be used in one or more of the following applications: imaging of blood oxygen saturation; imaging of tumor hypoxia; imaging of wound healing, burn diagnosis or surgery; imaging of the microcirculation; imaging blood oxygenation parameters; estimating blood flow in blood vessels flowing into and out of a tissue region; imaging of a molecular specific target; imaging angiogenesis in a preclinical tumor model; clinical imaging of microcirculatory and macrocirculatory pigment cells; imaging of the eye; fluorescein angiography is added or replaced; imaging a skin lesion; imaging melanoma; imaging basal cell carcinoma; imaging hemangiomas; imaging psoriasis; imaging eczema; imaging dermatitis; imaging the morse operation; imaging to verify tumor margin resection; imaging peripheral vascular disease; imaging diabetic ulcers and/or pressure ulcers; imaging burn; performing plastic surgery; microsurgery; imaging of circulating tumor cells; imaging melanoma cells; imaging lymph node angiogenesis; imaging a response to photodynamic therapy; imaging a response to photodynamic therapy with a vascular ablation mechanism; imaging the response to chemotherapy; imaging the response to the anti-angiogenic drug; imaging the response to radiation therapy; estimating oxygen saturation using multi-wavelength photoacoustic excitation; estimating venous oxygen saturation in the event that a pulse oximeter cannot be used; estimating cerebral venous oxygen saturation and/or central venous oxygen saturation; estimating oxygen flow and/or oxygen consumption; imaging vascular beds and depth of invasion in barrett's esophageal cancer and/or colorectal cancer; functional imaging during brain surgery; assessment of internal bleeding and/or cauterization verification; imaging perfusion sufficiency of the organ and/or organ transplant; imaging angiogenesis around islet transplantation; imaging of skin grafts; imaging the tissue scaffold and/or biological material to assess angiogenesis and/or immune rejection; assisting in imaging of microsurgery; avoid the guidance of cutting vessels and/or nerves; imaging of contrast agents in clinical or preclinical applications; identification of sentinel nodes; non-invasive or minimally invasive identification of tumors in lymph nodes; nondestructive testing of materials; imaging of a gene-encoded reporter gene, wherein the gene-encoded reporter gene may comprise tyrosinase, chromoprotein, and/or fluorescent protein for preclinical or clinical molecular imaging applications; imaging an actively or passively targeted optically absorbing nanoparticle for molecular imaging; imaging of thrombus; classifying the existence time of thrombus; replacement catheterization; gastroenterology application; single excitation pulse imaging over the entire field of view; imaging tissue; cell imaging; imaging of scattered light from the surface of the object; imaging of changes caused by absorption of scattered light; or optically absorptive non-contact imaging.
Aspects disclosed herein may provide a method of visualizing features in a sample. The method may comprise the steps of: receiving a signal over a period of time; determining a characteristic of the sample based on an evolution of the received signal over the period of time; and determining an image based on the determined features. The signal may include a non-radiated signal and a radiated signal from the sample.
The method may include: one of the received signals is divided into two or more instances, one of the instances is filtered, and the two or more instances are recorded on the two or more channels, respectively.
The method may include: the received signals are plotted along a first spatial axis, a second spatial axis, and a time axis, and a volume is calculated based on the plotted received signals.
The method may include: the determined image is displayed. The method may include: the determined image is displayed in conjunction with the secondary visualization. The secondary visualization may be a bright field image of the sample. The secondary visualization may appear as a background to the determined image.
Aspects disclosed herein may provide a method of visualizing features in a sample. The method may comprise the steps of: receiving a signal over a period of time; determining a characteristic of the sample based on an evolution of the received signal over the period of time; and determining an image based on the determined features. The received signals may be indicative of two or more unique absorption-based measurements in the sample. The two or more unique absorption-based measurements include a radiation measurement and a non-radiation measurement.
Aspects disclosed herein may provide a method of visualizing features in a sample. The method may comprise the steps of: receiving a signal; clustering the received one or more signals using a clustering algorithm to determine characteristics of the sample; and determining an image based on the cluster signal. The signal may include a non-radiated signal and a radiated signal from the sample. At least some of the received signals may be collected by: the method includes generating a signal in the sample at an excitation location using an excitation beam, interrogating the sample with an interrogation beam directed toward the excitation location of the sample, and detecting a portion of the interrogation beam returned from the sample. By detecting optical absorption and scattering from the sample, at least some of the signals may be collected.
The method may include: determining a cluster center based on the cluster signal; and determining a characteristic time domain signal based on the determined cluster center.
The clustering algorithm may be configured to: when executed, a predetermined number of signals are selected as an initial cluster center, cluster members of all signals are updated, an average residual and a change in the average residual are determined, the cluster center and the first principal component are determined, and whether a convergence criterion is met. Updating cluster members may include: the distance from each signal to each center is determined, and members are assigned to clusters that include centers with the smallest center distance.
The method may include: one or more colors used in the image are determined based on the cluster signal and the determined features.
Aspects disclosed herein may provide a light absorbing remote sensing system for imaging features in a sample. The system may include: an excitation light source, an interrogation light source, and a processor, the excitation light source configured to generate a signal in a sample at an excitation location; the interrogation light source is configured to interrogate the sample and is oriented toward an excitation location of the sample; the processor is configured to analyze the generated signal as a function of time and to determine an image. The excitation light source may be focused below the surface of the sample. The interrogating light source may be focused beneath the surface of the sample. A portion of the at least one interrogating light source returned from the sample may be indicative of at least some of the generated signals. The image may indicate features in the sample.
The filter may be configured to separate the return portion of the interrogating light source from the remainder of the light from the sample. The processor may be configured to analyze the signal indicated in the return portion of the sample and the signal indicated in the remaining portion of the light.
The processor may be configured to execute a clustering algorithm to cluster the generated signals and determine an image based on the clustered generated signals.
The at least one excitation light source may comprise a first excitation light source and a second excitation light source. The first excitation light source may be configured to provide light at a first wavelength and the second excitation light source may be configured to provide light at a second wavelength.
Aspects disclosed herein may provide a light absorbing remote sensing system for imaging features in a sample. The system may include: an excitation light source, an interrogation light source, a detection source, and a processor, the excitation light source configured to generate a signal in a sample at an excitation location; the interrogation light source is configured to interrogate the sample and toward an excitation location of the sample; the detection source is configured to detect light from the sample, the detection source may be configured to detect a portion of an interrogating light source returning from the sample, and the processor is configured to analyze the generated signal as a function of time and determine an image. The excitation light source may be focused below the surface of the sample. The interrogating light source may be focused beneath the surface of the sample. The return portion of the interrogating light source may be indicative of at least some of the signals generated. The image may indicate features in the sample.
The detection source may comprise a plurality of integrated photodetectors arranged such that the return portion of the interrogating light source may be distributed across the plurality of integrated photodetectors.
The plurality of integrated photodetectors may be configured such that there may be a tunable delay between integration start times of each photodetector. The detection source may include at least one of (i) one or more mechanical scanning stages and (ii) a resonant scanner or a polygonal scanner.
Aspects disclosed herein may provide a computer-implemented method of visualizing features in a sample. The method may comprise the steps of: receiving one or more light absorbing remote sensing (photoabsorption remote sensing, PARS) signals; clustering the received one or more PARS signals using a clustering algorithm to determine a characteristic of the sample; and determining an image based on the clustered PARS signals.
At least some of the PARS signals may be collected by: generating a signal in the sample at an excitation location using the excitation beam, interrogating the sample with an interrogation beam directed toward the excitation location of the sample, and detecting a portion of the interrogation beam returned from the sample. The excitation beam is focused below the surface of the sample. The interrogation beam may be focused below the surface of the sample.
Generating the signal may include: generating a pressure signal, a temperature signal and a fluorescence signal. The return portion of the interrogation beam may be indicative of the generated pressure signal and temperature signal. By detecting fluorescent signals from the excitation site of the sample when detecting the generated pressure signal and temperature signal, the PARS signal may be further collected.
Generating the signal may include: a radiated signal and a non-radiated signal are generated. The return portion of the interrogation beam may be indicative of the generated non-radiation signal. The PARS signal may be further collected by detecting the radiation signal from the excitation site of the sample at the same time as the generated non-radiation signal.
The PARS signal may be further collected by redirecting a portion of the returned interrogation beam and detecting interactions with the sample.
The wavelength of the excitation beam may be configured such that the sample absorbs two or more photons simultaneously. The sum of the energies of the two or more photons may be equal to the predetermined energy.
The method may include collecting the PARS signal. The received PARS signals may be clustered based on shape.
The method may not include analyzing the reconstructed grayscale image to determine the image. The received PARS signals may not be clustered based on scalar magnitude. The method may not include mapping or visualizing scalar amplitudes.
The PARS signal may be indicative of a temperature characteristic of the sample. The PARS signal may be indicative of the speed of sound in the sample. The PARS signal may be indicative of molecular information. The PARS signal may be indicative of a characteristic in a sample in a region having a size defined by the focused beam.
Receiving the PARS signal may include receiving a Time Domain (TD) signal.
The method may include: a cluster center is determined based on the clustered PARS signals. The determined cluster center may comprise a characteristic time domain signal.
Receiving the PARS signal may comprise: a back-scattered intensity, a radiated signal, and a non-radiated relaxation time domain signal are received.
Receiving the PARS signal may comprise: a radiated PARS signal and a non-radiated PARS signal are received. The method may further comprise: the ratio of the radiated to non-radiated PARS signals is determined.
The method may include: the decay time is determined based on the received PARS signal. Determining the image may include: one or more colors are determined based on the clusters.
The method may be used in one or more of the following applications: imaging of blood oxygen saturation; imaging of tumor hypoxia; imaging of wound healing, burn diagnosis or surgery; imaging of the microcirculation; imaging blood oxygenation parameters; estimating blood flow in blood vessels flowing into and out of a tissue region; imaging of a molecular specific target; imaging angiogenesis in a preclinical tumor model; clinical imaging of microcirculatory and macrocirculatory pigment cells; imaging of the eye; fluorescein angiography is added or replaced; imaging a skin lesion; imaging melanoma; imaging basal cell carcinoma; imaging hemangiomas; imaging psoriasis; imaging eczema; imaging dermatitis; imaging the morse operation; imaging to verify tumor margin resection; imaging peripheral vascular disease; imaging diabetic ulcers and/or pressure ulcers; imaging burn; performing plastic surgery; microsurgery; imaging of circulating tumor cells; imaging melanoma cells; imaging lymph node angiogenesis; imaging a response to photodynamic therapy; imaging a response to photodynamic therapy with a vascular ablation mechanism; imaging the response to chemotherapy; imaging the response to the anti-angiogenic drug; imaging the response to radiation therapy; estimating oxygen saturation using multi-wavelength photoacoustic excitation; estimating venous oxygen saturation in the event that a pulse oximeter cannot be used; estimating cerebral venous oxygen saturation and/or central venous oxygen saturation; estimating oxygen flow and/or oxygen consumption; imaging vascular beds and depth of invasion in barrett's esophageal cancer and/or colorectal cancer; functional imaging during brain surgery; assessment of internal bleeding and/or cauterization verification; imaging perfusion sufficiency of the organ and/or organ transplant; imaging angiogenesis around islet transplantation; imaging of skin grafts; imaging the tissue scaffold and/or biological material to assess angiogenesis and/or immune rejection; assisting in imaging of microsurgery; avoid the guidance of cutting vessels and/or nerves; imaging of contrast agents in clinical or preclinical applications; identification of sentinel nodes; non-invasive or minimally invasive identification of tumors in lymph nodes; nondestructive testing of materials; imaging of a gene-encoded reporter gene, wherein the gene-encoded reporter gene comprises tyrosinase, chromoprotein, and/or fluorescent protein for preclinical or clinical molecular imaging applications; imaging an actively or passively targeted optically absorbing nanoparticle for molecular imaging; imaging of thrombus; classifying the existence time of thrombus; replacement catheterization; gastroenterology application; single excitation pulse imaging over the entire field of view; imaging tissue; cell imaging; imaging of scattered light from the surface of the object; imaging of changes caused by absorption of scattered light; or optically absorptive non-contact imaging.
The method may include displaying the image on a display.
Aspects disclosed herein may provide a light absorption remote sensing (PARS) system for imaging features in a sample. The system may include: an excitation light source, an interrogation light source, and a processor, the excitation light source configured to generate a signal in a sample at an excitation location; the interrogation light source is configured to interrogate the sample and toward an excitation location of the sample; the processor is configured to execute a clustering algorithm to cluster the generated signals and to determine an image based on the clustered generated signals. The excitation light source may be focused below the surface of the sample.
The interrogating light source may be focused beneath the surface of the sample. A portion of the at least one interrogating light source may be returned from the sample, which returned portion may be indicative of the generated signal. The image may indicate features in the sample.
The method may include a display configured to display the determined image. The image may be formed directly from the received signal.
The processor may be configured to determine one or more colors based on the clusters. The determined colors include: purple, blue and pink, so that the image can be configured to resemble hematoxylin and eosin (hematoxylin and eosin, H & E) stained images.
Aspects disclosed herein may provide a computer-implemented method of visualizing features in a sample. The method may comprise the steps of: receiving one or more signals; clustering the received signals based on shape using a clustering algorithm to determine time domain features of the samples; an image is determined. The method may include: one or more colors used in the image are determined based on the cluster signal and the determined temporal features.
The method may include: a vector angle is determined from the received one or more signals. Clustering the received signals based on shape may include: the received signals are clustered based on vector angle. The one or more signals include at least one of a non-radiated signal or a radiated signal.
The one or more signals may include at least one of a non-radiative thermal signal or a non-radiative pressure signal. The one or more signals may include at least one of the radiated fluorescent signals. The radiative fluorescent signal may be a radiative autofluorescent signal.
Aspects disclosed herein may provide a computer-implemented method of visualizing features in a sample. The method may comprise the steps of: receiving a signal; clustering the received one or more signals using a clustering algorithm to determine characteristics of the sample; and determining an image based on the cluster signal. The signal may include a non-radiated signal and a radiated signal from the sample. The non-radiative signals may include thermal signals and pressure signals, and the radiative signals may include fluorescent signals.
At least some of the signals may be collected by: generating a signal in the sample at an excitation location using the excitation beam, interrogating the sample with an interrogation beam directed toward the excitation location of the sample, and detecting a portion of the interrogation beam returned from the sample. By detecting optical absorption and scattering from the sample, at least a portion of the signal may be collected. Excitation and detection of the sample may result in optical absorption and scattering.
Aspects disclosed herein may provide a method of visualizing features in a sample. The method may comprise the steps of: receiving one or more signals; clustering the received signals based on shape using a clustering algorithm to determine characteristics of the samples; an image is determined. The shape may be based on a vector. Determining the image may include: one or more colors used in the image are determined based on the cluster signal and the determined features.
Aspects disclosed herein may be used with and/or receive or collect signals from light absorbing or photoacoustic remote sensing systems, methods, or signals disclosed in any of the following U.S. patent applications: U.S. application serial No. 16/847,182 (invention name "Photoacoustic Remote Sensing (PARS) (photo-acoustic remote sensing)") submitted by 13 months in 2020, U.S. patent application serial No. 17/091,856 (invention name "Non-Interferometric Photoacoustic Remote Sensing (NI-PARS)") submitted by 6 months in 2020, U.S. application serial No. 16/814,538 (current U.S. patent No. 11,022,540) (invention name "Camera-Based Photoacoustic Remote Sensing (C-PARS)"), U.S. application serial No. 16/753,887 (invention name "Coherence Gated Photoacoustic Remote Sensing (CG-PARS)"), U.S. application serial No. 16/647,076 (invention name "Single Source Photoacoustic Remote Sensing (SS-PARS)") (single source photo-acoustic remote sensing) ") submitted by 13 months in 2020, U.S. application serial No. 16/629,371 (invention name" Photoacoustic Remote Sensing (photo-acoustic remote sensing) "), and related methods of" 20295 "), U.S. application serial No. 16/753,887 (invention name" Coherence Gated Photoacoustic Remote Sensing (CG-PARS) "), U.S. patent application serial No. 16/647 (invention name" Coherence Gated Photoacoustic Remote Sensing (co-PARS) "), U.S. 9,076 (invention name" method of "35 (co-PARS)") submitted by 13 months in 2020, and "5/9,394 (co-1, etc.), and method of" linear patent application serial No. 9,394 (invention name "being applied by" No. 35 (co-3,9843/Linear PARS Methods). The above application is incorporated herein by reference. Aspects disclosed herein may be used with any PARS system described in the above-referenced application, for example: time domain PARS OR TD-PARS, total absorption PARS OR TA-PARS, multipass PARS OR MP-PARS, multiphoton excitation PARS OR multiphoton PARS, thermally enhanced PARS OR TE-PARS, temperature sensing PARS OR TS-PARS, super resolution PARS OR SR-PARS, spectrally enhanced PARS OR SE-PARS, smart detection PARS OR SD-PARS, camera-based PARS OR C-PARS, non-interference PARS OR NI-PARS, coherence gating PARS OR CG-PARS, single source PARS OR SS-PARS, optical resolution PARS OR-PARS, dual mode PARS combined with optical coherence tomography (PARS-OCT), and/OR dual mode PARS combined with optical coherence tomography (EPARS-OCT).
The novel light absorption remote sensing (PARS) signal extraction algorithm may take advantage of various absorption-induced modulation effects, including but not limited to modulation of material reflectivity, scattering, polarization, phase accumulation, nonlinear absorption, nonlinear scattering, and the like. These can be used in multiplexed acquisitions to identify and/or unmixed, respectively, constituent chromophores from within a sample by using various excitation, detection and signal enhancement beam properties, including, but not limited to, variations in wavelength, pulse width, power, energy, coherence length, repetition rate, exposure time, etc. These properties may take any value suitable for the task. Common ranges may include: wavelength (nm to μm), pulse width (atto seconds to milliseconds), power (atto watts), pulse energy (atto joules), coherence length (nm to km), and repetition rate (continuous wave to gigahertz). In contrast to signal enhancement beams that can be implemented using relatively long pulse widths (nanoseconds and longer), the excitation beam can generally be implemented using shorter pulse widths (nanoseconds and sub-nanoseconds) that are intended to induce the PARS signal impulse response, as the signal enhancement beam need only induce thermal perturbations. For example, the pulse width of the excitation beam may be greater than 1ns, or less than 1ns; the pulse width of the signal enhancing beam may be higher. Excitation, detection, and signal enhancement wavelengths may be implemented using different wavelengths, pulse widths, time delays, or polarization states in a given system architecture to provide a means of optical differentiation between paths.
Other novel PARS signal extraction algorithms may use characteristic features of the collected time domain behavior to improve signal fidelity, enhance image contrast, and recover information about sample shape, size, and dimensions, or for performing multi-path/functional imaging. Processing techniques may include, but are not limited to, phase-locked amplification (software-based and hardware-based implementations), machine learning methods, generalized feature extraction, feature extraction based on multidimensional decomposition and frequency, and signal processing methods.
PARS can be used to unmixe the components of a target based on the absorption, temperature, polarization, frequency, phase, nonlinear absorption, architecture, velocity, fluorescence, nonlinear scattering, and scattering content of the target. It can also be used to unmixe the size, shape, characteristics and dimensions of the target based on its absorption, temperature, polarization, frequency, phase, nonlinear absorption, nonlinear scattering and scattering content. By utilizing different wavelengths, different pulse widths, different coherence lengths, repetition rates, laser exposure times, laser energy densities, the PARS signals can be used to unmixe targets using their absorption content, scattering content, fluorescence, polarization content, frequency content, phase content. The PARS signals may be dominated by the generated pressure and analyzed to provide information based on their amplitude/intensity, frequency content, content related to polarization changes, fluorescence, second harmonic generation, and phase changes. The PARS signals may be dominated by the temperature of generation and analyzed to provide information based on their amplitude/intensity, fluorescence, frequency content, second harmonic generation, content related to polarization changes, and phase changes. The PARS system may be configured to capture any optical absorption induced changes in the sample. Such changes may include whole non-radiative and radiative relaxation, such as pressure signals, temperature signals, ultrasound signals, autofluorescence signals, nonlinear scattering, and nonlinear fluorescence.
A portion of the interrogation, signal enhancement, excitation, or autofluorescence from the sample may be collected to form an image. These signals may be used to unmixe the size, shape, characteristics, size, nature, and composition of the samples. In a given architecture, any portion of the light returned from the sample may be collected, such as a detection, excitation, or thermally enhanced beam. The return light may be analyzed based on wavelength, phase, polarization, etc. to capture any absorption-induced signals, including pressure, temperature, and optical emissions. In this way, PARS can capture scattering, autofluorescence, and polarization contrast due to, for example, each detection, excitation, and thermal enhancement source simultaneously. Furthermore, the PARS laser source may be deliberately chosen to highlight these different contrast mechanisms.
Other aspects will be apparent from the following description and claims.
Drawings
In this patent document, the word "comprising" is used in its non-limiting sense to mean that items following the word are included, but items not specifically mentioned are not excluded. The element recited by the indefinite article "a" does not require the presence of and only the element.
The scope of the following claims should not be limited by the preferred embodiments set forth in the examples and figures above, but should be given the broadest interpretation consistent with the description as a whole.
Fig. 1 shows an overview of the PARS system.
Figure 2 shows an overview of the PARS system with PARS excitation and PARS detection.
Fig. 3 shows an implementation of PARS in combination with other modes.
Fig. 4 shows the signal processing path of the PARS signal.
Fig. 5 shows an exemplary architecture for Total Absorption (TA) PARS, with an autofluorescence detection system used as an example.
Fig. 6 shows the visualization generated by the autofluorescence sensitive total absorption PARS (TA-PARS) architecture.
Fig. 7 shows an exemplary signal evolution of the TA-PARS signal.
Fig. 8 shows an example of a radiated signal and a non-radiated signal.
Fig. 9 shows an exemplary architecture using two excitation sources, one detection source, and multiple photodiodes.
Fig. 10 shows a comparison of non-radiation absorption (view (a)), radiation absorption (view (b)) and scattering (view (c)) provided by a TA-PARS system.
Fig. 11 shows an example of TA-PARS imaging.
Fig. 12 shows an exemplary application of the quantum efficiency ratio (quantum efficiency ratio, QER).
Fig. 13 shows an example of TA-PARS imaging using a QER acquisition procedure.
Fig. 14 shows a comparison of imaging with conventional staining using the QER acquisition procedure.
Fig. 15 shows an exemplary PARS signal evolution.
Fig. 16 shows an example of life-span PARS images in resected murine brain tissue.
Fig. 17 shows an exemplary PARS signal evolution associated with the fast life extraction technique.
Fig. 18 illustrates an exemplary architecture for a multi-pass (MP) PARS system.
Fig. 19 compares multiphoton PARS with normal PARS.
Fig. 20A and 20B show the reconstructed greyscale PARS image and corresponding staining.
Fig. 21A and 21B illustrate the principal component of the time domain TD-PARS signal and the principal component-based synthetic staining.
Fig. 22 shows an exemplary architecture for analyzing TD-PARS signals.
Fig. 23 shows a plot of TD-PARS signal and center.
Fig. 24 shows a visualization using a clustering method.
FIG. 25 shows the visualization of three different areas of brain tissue using a clustering method.
Fig. 26 illustrates an exemplary clustering algorithm for analyzing the TD-PARS signals and determining images.
Fig. 27 illustrates a method of determining an image using a clustering algorithm.
Fig. 28 illustrates non-radiative signal extraction.
Fig. 29 illustrates examples of various filtering of the PARS signal.
Fig. 30 illustrates the expected spatial correlation between adjacent points or signals.
Fig. 31 illustrates two signals with different lifetimes in connection with functional extraction.
Fig. 32 shows a comparison of an original image and a denoised image.
Fig. 33 shows chirped pulse signal and acquisition.
Fig. 34 shows an exemplary TD-PARS acquisition by which a signal is reconstructed by applying a delay.
Fig. 35 illustrates data compression using digital and/or analog techniques.
Fig. 36 illustrates an exemplary rapid acquisition method.
Fig. 37 shows a direct construction of a color image.
Detailed Description
Reference will now be made in detail to examples of the present disclosure that are illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts. In the following discussion, relative terms such as "about," "substantially," "approximately," and the like are used to indicate possible variations of the numerical values.
A recently reported photoacoustic technique called photoacoustic remote sensing (photoacoustic remote sensing, PARS) microscopy (US 2016/011087 and US 2017/0215738) has solved many of these sensitivity problems by new detection mechanisms. Once acoustic pressure propagates away from its source, PARS is able to directly detect excited photoacoustic regions, rather than detecting acoustic pressure at the outer surface. This is achieved by monitoring changes in the optical properties of the material consistent with photoacoustic excitation. These changes then encode various important material properties such as optical absorption, physical target size, and constituent chromophores, etc.
Since the PARS device may use only two beams, which may be confocal arrangements, the spatial resolution of the imaging technique may be defined as either excitation-defined (ED) or interrogation-defined (ID), depending on which beams provide a tighter focus at the sample. This aspect may also facilitate imaging of deeper targets beyond the limitations of optical resolution devices. This can be achieved by using deep penetration (long transmission mean free path) detection wavelengths, such as short wave infrared (like 1310nm, 1700nm or 10 um), which can provide spatial resolution within highly scattering media (such as biological tissue) that is superior to the depth provided by a given excitation (such as 532nm or 266 nm). If more than two beams are used so that the system consists of more than two foci at the sample, these components are expected to expand significantly. For example, if an additional beam is added that amplifies the signal in its focal region, it may also help define the desired resolution of the system.
The intensity modulated PARS signal depends not only on the optical absorption and the incident excitation flux, but also on the detection laser wavelength, flux and temperature of the sample. The PARS signal may also result from other effects such as scatterer position modulation and surface oscillations. Similar simulations may exist for PARS devices that utilize other modulated optical properties such as intensity, polarization, frequency, phase, fluorescence, nonlinear scattering, nonlinear absorption, etc. Since the material properties depend on the ambient temperature, there is a corresponding temperature dependence in the PARS signal. At some intensity levels, additional saturation effects may also be utilized.
The above mechanism points to an important source of scattering location or scattering cross section modulation, which can be easily measured when the probe beam is focused to sense a limited excitation volume. However, these large local signals are not the only potential sources of PARS signals. Acoustic signals propagating to the sample surface may also cause variations in the PARS signal. These acoustic signals may also generate surface oscillations, which result in phase modulation of the PARS signal.
These generated signals may be intentionally controlled or affected by secondary physical effects such as vibration, temperature, stress, surface roughness, mechanical bending, etc. For example, a temperature may be introduced into the sample, which may enhance the generated PARS signal compared to the PARS signal generated without introducing the additional temperature. Another example may involve introducing mechanical stress (such as bending) to the sample, which in turn may affect the material properties of the sample (e.g., density or local optical properties such as birefringence, refractive index, absorption coefficient, scattering behavior) and thereby perturb the generated PARS signal compared to the PARS signal generated without introducing the mechanical stress. Additional contrast agents may be added to the sample to enhance the generated PARS signal, including but not limited to dyes, proteins, specifically designed cells, liquids, and optical agents or windows. The targets may be optically altered to provide optimized results.
Some techniques may simply monitor the intensity back reflection and may extract the amplitude of these time domain signals. However, additional information may be extracted from time-varying aspects of the signal. For example, some of the scattering, polarization, frequency, and phase content of a PARS signal may be attributable to the size, shape, characteristics, and dimensions of the region in which the signal is generated. This may encode unique/orthogonal additional information with utility for improving final image fidelity, classifying sample regions, sizing constituent chromophores, and classifying constituent chromophores, to name a few. Because such techniques may generate separate data sets for the same interrogated region, they may be combined or compared to one another. For example, the frequency information may describe the microstructure within the sample, which may be combined with conventional PARS that uses scatter modulation to highlight regions that are both absorptive and of a particular size.
Referring to fig. 1, photoacoustic remote sensing (photoacoustic remote sensing, PARS) microscopy is an all-optical non-contact optical absorption microscopy technique. PARS can use confocal excitation and detection laser pairs to generate and detect optical absorption contrast in various samples. In PARS, the excitation laser may comprise a pulsed excitation laser that may be used to deposit optical energy into the sample. When light is absorbed by the chromophore, photon energy is captured by the sample. The absorbed energy may then be dissipated by optical (radiative) or non-radiative relaxation. During non-radiative relaxation, the absorbed light energy is converted into heat. In some cases, the generation of heat may cause thermo-elastic expansion, thereby generating photoacoustic pressure. During radiation relaxation, the absorbed light energy is released by the emission of photons. Typically, the emitted photons exhibit a different energy level than the absorbed photons.
The local temperature and pressure variations cause nanosecond scale perturbations in the optical and material properties of the sample. The detection laser, which is co-focused with the excitation spot, as a scattering intensity modulation captures the absorption induced disturbances in the optical properties. By measuring the perturbations in the detection laser emission, then PARS can measure the non-radiative absorption contrast of different biomolecules. At the same time, by capturing the detected undisturbed back-reflection and back-reflected excitation energy, PARS can capture the optical scattering contrast due to excitation and detection sources, respectively.
Fig. 1 shows a high-level schematic diagram of a light absorbing remote sensing (PARS) system. This consists of a PARS system (101), an optical combiner (102) and an imaging head (104). The PARS system may further comprise other systems (e.g., signal enhancement systems), and the optical combiner may combine the beams from the PARS system (101) and these other systems.
Fig. 2 shows a high-level schematic diagram depicting PARS excitation (202), PARS detection (204), and optical combiner (203). These may be combined with other systems (e.g., signal enhancement systems) and imaging heads (205).
Fig. 3 shows a high level embodiment of the PARS system in combination with other modes (305). This consists of a PARS system (301), an optical combiner (302) and an imaging head (304). These may be combined with various other modes (305), such as bright field microscopy, scanning laser ophthalmoscope, ultrasound imaging, stimulated raman microscopy, fluorescence microscopy, two-photon and confocal fluorescence microscopy, coherent-anti-raman-stokes microscopy, raman microscopy, other PARS, photoacoustic and ultrasound systems, and the like.
Fig. 4 shows a signal processing path. This is composed of a photodetector (401), a signal processing unit (402), a digitizer (403), a digital signal processing unit (404), and a signal extraction unit (405).
TA-PARS
When the sample absorbs light, there is a limited number of interactions that may occur. The absorbed energy is converted into temperature and pressure, or into light of a different wavelength. When the temperature signal and the pressure signal are captured by the PARS detection beam, the light emission may be detected by a Total Absorption (TA) PARS system, which may be sensitive to radiation relaxation. In this way, all or nearly all of the tissue's absorption of light (whether pressure generated in the form of non-radiative signals, etc., temperature generated, radiative relaxation, such as fluorescence, multiphoton fluorescence, or stimulated raman scattering) and/or scattering signals (such as local scattering signals) may be captured by the PARS.
Fig. 5 shows an exemplary architecture of radiation relaxation sensitive PARS. As an example, the radiative relaxation may be fluorescent or autofluorescent, but the aspects disclosed herein are not limited. For example, the radiative relaxation may include raman scattering, fluorescence, autofluorescence, multiphoton fluorescence, and the like. For convenience of description, an autofluorescence sensitive TA-PARS system will be described as an example with reference to fig. 5. A multi-wavelength fiber excitation laser (5812) is used to generate the PARS signal. The excitation beam (5817) passes through a multi-wavelength cell (5840) and a lens system (5842) to adjust its focus on the sample (5818). The optical subsystem for adjusting the focus may be constructed from components known to those skilled in the art including, but not limited to, beam expanders, adjustable collimators, adjustable reflection expanders, telescopic systems, and the like.
The signal characteristics are interrogated using a short or long coherent length probe beam (5816) from a detection laser (5814) that is co-focused and co-aligned with the excitation spot on the sample (5818). The interrogation/detection beam (5816) passes through a lens system (5843), polarizing beam splitter (5844) and quarter wave plate (5856) to direct reflected light (5820) from the sample (5818) to the photodiode (5846). However, such an architecture is not limited to including a polarizing beam splitter (5844) and a quarter wave plate (5856). The foregoing components may replace fiber-based equivalent components such as circulators, couplers, faraday rotators, electro-optic modulators, WDM and/or double-clad fibers, which are non-reciprocal elements. Such an element may receive light from a first path, but then redirect the light to a second path.
An interrogation beam (5816) is combined with the excitation beam using a beam combiner (5830). The combined beam (5821) is scanned by a scanning unit (5819). This passes through the objective lens (5855) and is focused onto the sample (5818).
The reflected beam (5820) returns along the same path. The reflected beam is filtered by a beam combiner/splitter (5831) to optically separate the detection beam (5816) from any autofluorescence returned from the sample. The autofluorescence light (5890) passes through a lens system (5845) to adjust its focus on the autofluorescence sensitive photodetector (5891). The isolated detection beam (5820) is transmitted through a beam splitter (5831) toward a signal collection/analysis path. Here, the returned detection light is redirected by the polarizing beam splitter (5844). The detection path is comprised of a photodiode (5846), an amplifier (5858), a fast data acquisition card (5850), and a computer (5852). The autofluorescence sensitive photodetector may be any device including a camera, photodiode array, etc. The autofluorescence detection path may include further beam splitters and photodetectors to further isolate and detect light of a particular wavelength.
FIG. 6 shows an exemplary visualization that may potentially be provided by autofluorescence sensitive TA-PARS. Any portion of the light returned from the sample (excluding the detection beam) may be collected and analyzed based on wavelength. By separating light emissions of a particular wavelength from the sample, we can see a particular molecule of interest. For example, we can apply autofluorescence sensitive PARS to imaging tissues. Here we select the PARS excitation to capture the absorption contrast of the nucleus. In this case we use UV excitation to generate pressure and temperature signals for the nuclei in the tissue. At the same time, we captured the auto-fluorescence contrast generated by PARS excitation. In this case, the non-nuclear region of the tissue is highly fluorescent. In this way we can provide a visualization of both nuclear and uncore structures in the tissue. Furthermore, the resulting visualization may only require a single (or only one or exactly one) excitation wavelength to capture. As previously described, the method may be used with other radiation relaxation sensitive PARS and may generate and capture radiation relaxation other than autofluorescence.
For example, the PARS radiation signal may be applied in a PARS absorption spectrometer to accurately measure all light absorption of a sample. Furthermore, we can use the radiation relaxation (e.g., autofluorescence in fig. 5) sensitive PARS to measure the proportion of absorbed energy converted to heat and pressure or light, respectively. This may enable sensitive quantum efficiency measurements in a wide variety of biological and non-biological samples.
The TA-PARS signal may also be collected on a single (only one or exactly one) detector, as highlighted in fig. 7. Assuming that the important components of the TA-PARS signal may appear to be different from each other, a single detector may properly characterize these components. For example, the initial signal level (scatter) may indicate the undisturbed intensity reflectivity of the detection beam of the sample at the interrogation location encoding the scatter intensity. Then, after excitation by an excitation pulse (100 ns in fig. 7), the PARS excitation signal associated with non-radiative relaxation (e.g., heat, temperature) and radiative relaxation (e.g., fluorescence or autofluorescence) can be observed as characteristic overlapping signals (labeled PA and AF in the figure).
If the excitation signals are significantly specific to each other (e.g., in amplitude or amplitude and/or evolution time), they can be decomposed from the combined signal to extract the amplitudes and their characteristic lifetimes. This rich information can be used to improve the available contrast, provide additional multiplexing capability, and provide unique molecular features that make up the chromophore. Furthermore, this approach may provide practical benefits because only a single detector and a single (only one or exactly one) detection path may be required, thereby greatly reducing physical hardware complexity and cost. Acquisition of signals over time is discussed in more detail in the section covering TD-PARS.
Referring to fig. 8, any given PARS excitation event always generates a fraction of the radiative and non-radiative relaxation. TA-PARS helps to capture the full absorption spectrum of chromophores. Thermal and pressure disturbances may generate corresponding modulations in the local optical properties. TA-PARS microscopy can capture the visualization of the scattering and total absorption (radiative and non-radiative relaxation) of chromophores in a single (only one or exactly one) excitation event. Non-radiative relaxation results in modulation by heat and pressure, which in turn results in a change in the back-reflected intensity in the detection beam. The PARS signal is expressed as reflectivity timesIncidence detection (RI) det ) Is a variation of (c). The radiation absorption path captures light emissions resulting from radiation relaxation, such as stimulated raman scattering, fluorescence, multiphoton fluorescence, and the like. The emission is expressed as a few wavelengths and energy light emissions (hv em ). The local scattering contrast is captured as the unmodulated back scatter of the detection beam (pre-excitation pulse). The scattering contrast is expressed as the undisturbed scattering distribution multiplied by the incident detection power (σ s I det )。
In TA-PARS, the modulation caused by non-radiative relaxation is detected by the probe beam at the excitation site. PARS can then visualize any thermal or photoacoustic pressure that leads to local optical property modulation. Meanwhile, TA-PARS utilizes additional detection paths to capture nonspecific optical emissions regardless of properties from the sample such as wavelength, frequency, polarization (excluding excitation and detection). These emissions can then be attributed to any radiative relaxation effects, such as stimulated raman scattering, fluorescence, and multiphoton fluorescence.
The use of this detection pathway can enhance the sensitivity of detection of any range of chromophores. Unlike conventional mode states that capture some radiation or non-radiation absorption independently, in TA-PARS, contrast may not be limited by efficiency factors such as photo-thermal conversion efficiency or fluorescence quantum yield. In addition to excitation and detected scattering, by capturing non-radiative and radiative absorption contrast, TA-PARS can capture all or nearly all optical properties of chromophores, such as absorption coefficient, scattering coefficient, quantum efficiency, nonlinear interaction coefficient, while providing detection sensitivity for most chromophores.
Quantum efficiency ratio (Quantum Efficiency Ratio, QER) and label-free H&E visualization
Both radiation absorbing and non-radiation absorbing portions can be captured and additional information can be generated. TA-PARS can produce an absorption metric proposed as a Quantum Efficiency Ratio (QER), which will visualize the biomolecule ratio radiation and non-radiation absorption responses. TA-PARS can provide label-free visualization of a range of biomolecules, enabling convincing analogs to be able to perform traditional histochemical staining of tissues, effectively providing label-free hematoxylin and eosin (H & E) like visualization.
QER can be defined as the radiated PARS signal (P r ) Non-radiative PARS (P nr ) For example:this ratio will be unique for any given chromophore. For example, a biomolecule-like collagen will exhibit high radiation contrast and low non-radiation contrast providing a high QER. In contrast, DNA will exhibit low radiation contrast and high non-radiation contrast providing a high QER. Calculating QER from radiation absorption and non-radiation absorption may additionally allow extraction of properties such as chromophore composition, density and quantity in a single (only one or exactly one) event. This may also allow for single-function imaging.
For example, picosecond pulsed excitation lasers can cause both radiative and non-radiative (thermal and pressure) perturbations in the sample. The thermal and pressure disturbances generate corresponding modulations in the local optical properties. As the back-scattered intensity changes, the secondary probe beam confocal with the excitation can capture the non-radiation absorption induced modulation of the local optical properties.
These backscatter modulations can be directly related to the local non-radiation absorption contrast. By the nature of the detection architecture, undisturbed back-scattering (pre-excitation events) also captures the scattering contrast seen by the detection beam. Unlike conventional photoacoustic methods, TA-PARS detection can instantaneously detect the modulation induced at the excitation site, rather than relying on pressure waves propagating through the sample prior to acoustic transducer detection. Thus, TA-PARS provides a non-contact procedure, facilitating imaging of fine and sensitive samples, which would otherwise be impractical for imaging with conventional contact-based PAM methods.
Because TA-PARS can rely solely on the generation of heat and subsequent pressure to provide contrast, the absorption mechanism is non-specific and highly sensitive to small changes in relative absorption. This allows the use of PARS to detect any kind of absorption mechanism such as vibration absorption, stimulated raman absorption and electron absorption. Previously, PARS has demonstrated label-free non-radiative absorbance contrast of hemoglobin, DNA, RNA, lipids, and cytochromes in samples such as chicken embryo models, resected tissue samples, and live murine models. In TA-PARS, a unique secondary detection path captures the radiation relaxation contrast in addition to non-radiation absorption. The radiation absorption path is designed to broadly collect all light emissions of any wavelength of light other than excitation and detection. Thus, the radiation detection path captures non-specific optical emissions from the sample, regardless of properties such as wavelength, frequency, polarization.
Referring to fig. 9, to improve the sensitivity of TA-PARS and facilitate detection of radiation absorption contrast, TA-PARS 900 may include: excitation at first and second excitation wavelengths that are different from each other (e.g., 266nm and 515nm excitation) to provide detection sensitivity to DNA, heme protein, NADPH, collagen, elastin, amino acids, various fluorescent dyes. The TA-PARS may comprise: with a dichroic filter and an avalanche photodiode to isolate and detect radiation absorption contrast. As illustrated in fig. 9, the TA-PARS system may comprise: excitation from a first excitation source 920 at a first excitation wavelength (e.g., visible light such as 515nm visible excitation) and excitation from a second excitation source 940 at a second excitation wavelength (e.g., UV light such as 266nm UV excitation). The first excitation source 920 may include a first excitation laser 902, such as a 50kHz to 2.7MHz 2ps pulsed 1030nm fiber laser (e.g., YLPP-1-150-v-30, IPG Photonics), although the aspects disclosed herein are not so limited. The second harmonic may be generated using lithium triborate crystals or LBO 922. The first (e.g., 515 nm) harmonic may be separated via dichroic mirror 906, spatially filtered with pinhole 908 prior to use in the imaging system. First excitation source 902 may include one or more lenses or waveplates, such as a half-wave plate or HWP 924 disposed between LBO 922 and first excitation laser 902, a filter lens, and/or a lens assembly 928. For example, a pinhole 908 may be provided between two lenses or lens assemblies 928.
The second excitation source 940 may include a second excitation laser 904, such as a 50khz 400ps pulsed diode laser (e.g., ridge XF 266, rpmc), although aspects disclosed herein are not so limited. The output from the second excitation laser 904 may be separated from the residual excitation (e.g., 532nm excitation) using prism 910 and then expanded prior to use in the imaging system (e.g., using a variable beam expander or VBE 926).
The TA-PARS system may include a detection system 950 that is shared by a first excitation source 920 and a second excitation source 940. As illustrated in fig. 9, the TA-PARS detection system 950 may include a probe beam 912, which may include a 405nm laser diode, such as a 405nm OBIS-LS laser (OBIS LS 405, coherent). Here, the detection may be coupled into the system by circulator 914 fiber, where the detection may be combined with excitation via one or more dichroic mirrors 916 and/or directed via mirror 934. The combined excitation and detection may be confocal onto the sample using a lens 918 (such as a 0.42NAUV objective lens). Back reflection detection from the sample may be returned to the circulator 914 through the same path as the forward propagation. The back reflection detection contains the PARS non-radiative absorption contrast as nanosecond intensity modulation that can be captured with a photodiode. The detection system 950 may also include a collimator and/or collimating assembly 936 to collimate the detection light.
The detection wavelength increases the scattering resolution, which improves copolymerization Jiao Chongdie between PARS excitation and detection spots on the sample. In combination with the circulator-based probe beam path and avalanche photodetector, TA-PARS has improved sensitivity compared to previous implementations. The visible wavelength detection also improves compatibility between visible and UV excitation wavelengths.
The radiation relaxation from each of the first and second excitations (266 nm and 515nm excitations) may be captured independently with different (or first and second) photodiodes 930 and 932. The radiation relaxation caused by the first excitation (radiation relaxation caused by 515 nm) may be isolated with dichroic mirror 916 and then captured using first photodiode 930. The radiation relaxation caused by the second excitation (266 nm induced radiation relaxation) may be isolated by redirecting a portion (e.g., 1% -50%) of the total light intensity returned from the sample toward the photodetector and/or the second photodiode 932. The light may then be spectrally filtered (e.g., via lens assembly 936) to remove residual excitation and detection prior to measurement.
To form an image, a mechanical stage may be used to scan the sample over the objective lens. Excitation sources 920 and 940 may be continuously pulsed (e.g., at 50 kHz) while stage speed may be adjusted to achieve a desired pixel size (interval between interrogation events). Each time the laser 902 and/or 904 is pulsed, a collection event may be triggered. During a collection event, a high speed digitizer (e.g., RZE-004-200, gageapplied) may be used to collect a few hundred nanosecond segments from the 4 input signals. These signals may include laser input reference measurements (excitation and detection), PARS scattering signals, PARS non-radiative relaxation signals, PARS radiative relaxation signals, and position signals from the stage. The time resolved scatter, absorption and position signals may then be compressed into a single characteristic feature. This will greatly reduce the capacity of data capture during collection.
To reconstruct the absorption and scattering images, the raw data may be fitted to a Cartesian grid based on the position signals at each interrogation. The original image may then be gaussian filtered and rescaled based on the histogram distribution prior to visualization.
TA-PARS visual fidelity is assessed by one-to-one comparison with traditional H & E stained images. The TA-PARS total absorption and QER contrast mechanisms were also demonstrated in a range of dye and tissue samples. In various fluorescent dyes and tissues, a high correlation between the radiation relaxation properties and the TA-PARS-measured QER was shown. These QER visualizations are used to extract regions of specific biomolecules (such as collagen, elastin, and nuclei) in tissue samples. This enables a wide range of applications of high resolution absorption contrast microscopy systems. TA-PARS can provide unprecedented label-free contrast in any kind of biological sample, thereby providing a visual effect that is difficult to obtain.
Fig. 10 shows a comparison of three different comparisons (non-radiation absorption in view (a), radiation absorption in view (b) and scattering in view (c)) provided by the TA-PARS system using 266nm excitation in thin sections of formalin fixed paraffin embedded (formalin fixed paraffin embedded, FFPE) human brain tissue. The non-radiative relaxation signal is captured based on nanosecond pressure and temperature induced modulation in a back-scattered 405nm detection beam collected from the sample. The radiation absorption contrast is captured as light emissions from the sample (except for the excitation and detection wavelengths blocked by the optical filter). At the same time, the unperturbed backscatter detected at 405nm captures local optical scatter from the sample. By this contrast, most of the important tissue structures are captured. The non-radiation absorption contrast is predominantly prominent in the nuclear structure, while the radiation contrast captures extranuclear features. The optical scattering contrast captures the morphology of thin tissue slices. In resected tissue, this scattering contrast becomes less suitable and is therefore not studied in other samples.
Fig. 11 shows an example of TA-PARS imaging. In view (a), TA-PARS captures the epithelial layer at the edge of resected human skin tissue. The stratum corneum is captured simultaneously in both radiation and non-radiation visualization. The radiation visualization provides improved contrast in restoring these tissue layers compared to the non-radiation image. In view (b), in another subcutaneous region of resected human skin tissue, TA-PARS captures connective tissue with sparse nuclei and elongated fibrin features.
The proposed system is also applied to image resected unprocessed murine brain tissue. In view (c), TA-PARS acquisition highlights the gray matter layer in the brain revealing the dense region of the nuclear structure. In the non-radiative image, the nuclei of the grey matter layer exhibit a higher contrast relative to the surrounding tissue compared to the radiative representation. Since the nuclei do not provide significant radiation contrast, the nuclear structure in the radiation image appears as voids or lack of signal within the sample. Although some potential kernels may be observed, they may not be identified with a significant confidence level as compared to those in the TA-PARS non-radiative representation. Along the upper right of the non-radiative acquisitions, structures resembling marrow neurons are identified, which surround the sparsely populated nuclei in this region.
In view (d), further acquisitions in the neighborhood emphasize the apparent myelogenous neuronal structure. Within these regions, dense structures of networks indicative of overlapping and interconnected dendrites and axons are apparent, with closely woven neuronal projections observed to be disposed around voids in tissue. Then, zoom out to a nearby larger imaging field, in view (e), a different tissue slice is restored with non-radiative contrast. The left side of the field contains dense bundles, indicating that myelin protrudes towards potentially gray matter with larger nuclei, as opposed to the right side, which is potentially white matter containing more myelinated structures with reduced nuclear density.
Referring to fig. 12, the ratio of qer or non-radiation absorbing moiety to radiation absorbing moiety is expected to contain more biomolecule-specific information. Ideally, the local absorption portion should be directly related to the radiation relaxation properties. The relative radiated and non-radiated signal intensities may be plotted and the QER may be plotted from the reported Quantum Efficiency (QE) values.
In one example, TA-PARS is used to measure a range of fluorescent dyes with different quantum efficiencies. 515nm excitation is used to generate both the radiation and non-radiation relaxation signals that are captured simultaneously.
As shown in view (a) in fig. 12, examples of relative radiated and non-radiated signal intensities are plotted. QER is then plotted against the QE values of the reported samples, as shown in view (b). Radiate PARS signal (P r ) It is expected that as QE increases linearly (P r Oc QE), but not radiate the PARS signal (P nr ) It is expected that as QE decreases linearly (P nr C 1-QE). Thus, the fractional relationship between the non-radiated and radiated signals is represented by the quotient of the linear functions (qer=pr/Pnr. Oc. QE/(1-QE)). The empirical results agree well with this expected model (r=0.988).
Fig. 13 illustrates an image from a QER acquisition process applied to imaging of thin slices of FFPE human tissue. Based on the non-radiation signal and the radiation signal, the QER for each image pixel is calculated, generating a QER image. In addition to the independent absorption factors, the results represent a dataset encoding chromophore-specific properties. QER processing helps to further separate similar tissue types from separate radiation or non-radiation acquisitions.
The colored version of the QER image shown in fig. 13 highlights various tissue components. The low QER biomolecules (DNA, RNA, etc.) may appear in a first color (e.g., a color having a lower wavelength or a bluish color), while the high QER biomolecules (collagen, elastin, etc.) may appear in a second color and/or a third color that is different from the first color (e.g., pink and purple) (e.g., has a wavelength higher than the first color). Collagen and elastin (which may appear as a fourth color or dark red) that make up fibrous connective tissue can be readily identified due to the low QER of collagen and elastin, as compared to H & E visualization captured after the QER imaging session (fig. 13, view (c-ii)). In contrast, the core structure is perceivable in the first color and/or the fifth color (e.g., blue) due to the high QER of the core structure. In QER visualization, connective tissue surrounding cancer cells is also distinguished from fibrous connective tissue in a sixth color (e.g., purple) as compared to H & E stained images. Complementary imaging contrast is provided when calculating QER from TA-PARS, further enabling chromophore specific ratios to be obtained independently using either radiative or non-radiative modes. Although the terms first color, second color, third color, fourth color, fifth color, and sixth color are used, aspects disclosed herein may not be limited to six predetermined colors, etc. The colors appearing in the visualization may have wavelengths proportional to QER. For example, a structure with a higher QER may appear as a color with a higher wavelength (e.g., red), while a structure with a lower QER may appear as a color with a lower wavelength (e.g., blue).
Although the QER method presented herein relies on extracted intensity values, similar analogs are contemplated that relate to similar these ratios for other signal parameters (such as lifetime, rise time, signal shape, frequency content, etc.).
Label-free histological imaging
The TA-PARS mechanism may provide the opportunity to accurately mimic traditional histological staining contrasts (such as H & E staining), and TA-PARS may provide label-free histological imaging. The non-radiative TA-PARS signal contrast may be similar to the non-radiative TA-PARS signal contrast provided by hematoxylin staining, while the radiative TA-PARS signal contrast may be similar to the radiative TA-PARS signal contrast provided by eosin staining. TA-PARS can capture unlabeled features such as adipocytes, fibrin, connective tissue, neuronal structures and nuclei. The intra-nuclear structures may be visualized with sufficient sharpness and contrast to identify individual atypical nuclei.
Fig. 14 shows an example of label-free histological imaging applied to FFPE human brain tissue. Referring to fig. 14, the non-radiative TA-PARS signal contrast is similar to that provided by hematoxylin stained nuclei (fig. 14, view (a)). Sections of FFPE human brain tissue were imaged using non-radiative PARS (fig. 14, view (a-i)). The non-radiative information was then stained to simulate a comparison of hematoxylin staining (fig. 14, view (a-ii)). The same tissue sections were then stained with hematoxylin alone and imaged under a bright field microscope (fig. 14, view (a-iii)), providing a direct one-to-one comparison. These visualizations are expected to be highly similar, as the main target of hematoxylin staining and the non-radiative portion of TA-PARS are nuclei, although other chromophores will also function to some extent.
A similar method was applied to eosin staining in adjacent sections. Adjacent slices were imaged using radiation PARS (fig. 14, view (b-i)). The radiation information was then colored to simulate a contrast of eosin staining (fig. 14, view (b-ii)). The sections were then stained with eosin (fig. 14, view (b-iii)) providing a direct one-to-one comparison of radiation contrast and eosin staining. In each of the TA-PARS and eosin stained images, similar microvasculature and red blood cells were resolved throughout the brain tissue. These visualizations are unexpected because the primary targets of the radiating portion of TA-PARS include heme protein, NADPH, flavins, collagen, elastin and extracellular matrix, closely reflecting chromophores targeted by eosin staining of extranuclear materials.
Since the different contrast mechanisms of TA-PARS closely mimic the visualization of H & E staining, the proposed system can provide true H & E-like contrast in a single (only once or just once) acquisition. TA-PARS can significantly improve visualization compared to the previous PARS simulated H & E system, which relies on scattering microscopy to estimate eosin contrast. Scattering microscopy-based methods do not provide clear images in complex scattering samples such as human tissue from a block resection. In contrast, TA-PARS can directly measure the extra-nuclear chromophore via a radiocontrast mechanism, providing a similar contrast to H & E regardless of sample morphology. Here, a combination of different TA-PARS visualizations using linear color blending is used to generate an effective representation of traditional H & E staining in undyed tissue.
An example in resected FFPE human brain tissue is shown in view (c) of fig. 14. The wide field image highlights the boundaries of cancerous and healthy brain tissue.
For qualitative comparison of TA-PARS with conventional H & E images, a series of human breast tissue sections were scanned with TA-PARS (FIG. 14, view (d-i) and FIG. 14, view (E-i)), then stained with H & E dye and imaged under a bright field microscope (FIG. 14, view (d-ii) and FIG. 14 (E-ii)). The TA-PARS simulated H & E visualization is effectively identical to the H & E formulation. In both images, clinically relevant features of metastatic breast lymph node tissue are equally readily available.
Lifetime imaging
The H & E simulation may be enhanced by extracting temporal features, which is discussed in more detail in the section below discussing TD-PARS and feature extraction imaging. While the total amplitude of the PARS modulation captures the local absorption of the excitation, the evolution of the modulation caused by pressure and temperature will also capture the local material properties.
Fig. 15 illustrates the evolution of the PARS signal over time. Each PARS excitation event will capture the scattered, radiated emission and PARS non-radiated relaxation time domain signals of the detection and excitation sources. Referring to fig. 15, pars decay or evolution time may be related to metrics such as thermal and pressure confinement times that control conventional photoacoustic imaging. This means that properties such as thermal diffusivity, conductivity, and sonic velocity may affect the PARS relaxation time. By measuring decay or evolution time, PARS can then further provide chromophore specific information about the sample. This may enable the chromophore to be unmixed (e.g., detect, separate, or otherwise discrete constituent species and/or subspecies) from a single excitation event or single-function imaging.
An example of a life-span PARS image in resected murine brain tissue is shown in fig. 16. Here, the core (which may appear as a first color such as white) is unmixed with the surrounding gray matter (which may appear as a second color such as green) and the interwoven myelogenous neuron structure (which may appear as a third color such as orange). The unmixing is performed based on the PARS lifetime signal.
Referring to fig. 17, a fast life extraction technique may be used to greatly enhance the PARS collection contrast. Referring to fig. 17, the pars amplitude may be calculated as the difference between the average pre-excitation signal and the post-excitation signal. Such acquisition is less sensitive to imaging noise than alternative extraction techniques. Previously, PARS used a min-max acquisition signal method to extract PARS specific signals. By subtracting the maximum from the minimum of the acquisition signal, PARS can emphasize the total amplitude of the PARS modulation. However, this is very susceptible to collection and measurement noise in the PARS signal.
One possible signal extraction method may be performed by determining an average pre-excitation signal. The averaged post-excitation signal is then calculated from the initial portion of the lifetime signal. The PARS amplitude is then calculated as the difference between the two average signals. This metric for rapid signal extraction greatly improves signal-to-noise ratio and sensitivity when collecting PARS signals. Because this technique relies on an average signal, the sensitivity of PARS acquisition to acquisition noise is greatly reduced.
Other time-based imaging methods will be discussed in more detail in the section below regarding TD-PARS and feature extraction imaging. First, two other PARSs will be briefly discussed.
MP-PARS
Referring to fig. 18, in multichannel PARS (MP-PARS), a backscatter detection can be captured and then redirected back to the sample where it again interacts with the sample before detection. Each time the assay interacts with the sample, it can obtain further information of the PARS modulation.
In PARS, a secondary confocal detection laser is used to visualize disturbances in optical properties caused by non-radiative absorption. The detection laser is confocal with the excitation spot such that the absorption-induced modulation can be captured as a change in the back-scattered intensity of the detection laser. For a given detection intensity I det Before the excitation pulse interacts with the sample, the signal may be approximated based on the following relationship: PARS (part) pre-ext ∝I det (R), wherein R is the undisturbed reflectivity of the sample.
Once the excitation pulse interacts with the sample, the signal can be approximated as: PARS (part) post-ext ∝I det (R+ΔR), wherein the pressure and temperature induced reflectance changes are denoted ΔR. The overall PARS absorption contrast is then approximated as: PARS (part) sig ∝PARS post-ext -PARS pre-ext . The previous relation PARS pre-ext And PARS post-ext Substitution, obtaining: PARS (part) sig ∝I det (R+ΔR)-I det (R)。
Prior to the excitation pulse, the backscattering of MP-PARS is approximated based on the following relationship: MPPARS pre-ext ∝(I det (R)) n Where R is the undisturbed reflectivity of the sample and n is the number of excitations interacting with the sample. Once the excitation pulse interacts with the sample, the signal can be approximated as: MPPARS post-ext ∝(I det (R+ΔR)) n Wherein the pressure and temperature induced reflectance changes are denoted as ΔR.
The total MP-PARS absorption contrast is then approximated as: MPPARS sig ∝MPPARS post-ext -MPPARS pre-ext . Substituting the previous relationship into MPPARS pre-ext And MPPARS post-ext The following results were obtained: MPPARS sig ∝(I det (R+ΔR)) n -(I det (R)) n Where n is the number of times the interaction with the sample is detected. Detection of these repeated interactions with the sample by backscatteringThe PARS signal may be extended non-linearly. The detection may then be redirected to interact with the sample any number of times, resulting in a corresponding degree of nonlinear expansion in the non-radiation absorption contrast.
The MP-PARS architecture (such as architecture 1800 illustrated in fig. 18) may be oriented such that by being composed of reflection or transmission events, these reflection or transmission events may occur at normal incidence or at some related transmission or reflection angle of the sample. For example, if the target has a particularly strong Mie scattering angle, it may be advantageous to orient multiple passes along this direction. Multiple passes may occur along a single (only one or exactly one) path, such as normal incidence reflection, or along multiple paths, such as normal incidence transmission architectures, even architectures with additional (more than two) paths to take advantage of additional spatial nonlinearities.
For example, MP-PARS architecture 1800 may comprise: excitation source 1802 (e.g., 266nm excitation source or laser), one or more detection sources 1804 (e.g., 405nm detection source or laser), one or more photodiodes or photodetectors 1806, circulator 1808, collimator 1810, one or more mirrors 1810 for directing excitation and/or detection light, prism 1816, and variable beam expander 1818. Further, MP-PARS architecture 1800 may comprise: a pair of alignment mirrors 1820 to align the excitation and/or detection light, and one or more scanners or scanning heads 1822, 1824 disposed on different sides of the sample. The one or more scanners may include: a first scanner 1822 for transmitting excitation and detection light to the sample, and a second scanner 1824 arranged with the mirror 1826 to allow multiple passes. The computer 1828 may be used to analyze the received signals and/or control the excitation source 1802 and the detection source 1804.
MP-PARS may be used as an optical amplifier for the detected PARS signal. The sensitivity of the measured signal can be further increased in the same way as a laser cavity system or photomultiplier tube is implemented. This may lead to a significant improvement in PARS imaging fidelity. PARS may be captured with increased sensitivity to any or all of radiation, non-radiation, or scattering contrast, thereby facilitating acquisition at lower imaging powers. This may facilitate the acquisition of lower concentrations of chromophores, chromophores with lower light blood absorption, or reduce sample exposure. By utilizing nonlinear spatial correlation to provide super-resolution imaging, these nonlinear effects can be utilized to improve the recovered imaging resolution.
Multiphoton excitation PARS
Referring to fig. 19, multiphoton PARS may provide several benefits over conventional PARS excitation. In multiphoton excitation, multiple photons are absorbed by the target at about the same time and/or in a single (only one or exactly one) event. The energies of these photons are then added together so that the absorbed photons are equivalent to a single (only one or exactly one) higher energy and shorter wavelength photon. Here, two photons with half the energy and twice the wavelength of a single photon excitation event are absorbed by the chromophore, providing a similar excitation.
In PARS, as in fluorescence microscopy, a nonlinear absorption mechanism can be utilized. Traditionally, PARS targets single photon absorption effects such as 266nm UV excitation of DNA. However, PARS may also be directed to multiphoton absorption characteristics, such as those used in multiphoton fluorescence microscopy. In a multiphoton microscope, many photons are absorbed by a target at about the same time. The energies of these photons are then added together so that the absorbed photons are equivalent to a single photon of higher energy and shorter wavelength.
In the case of two-photon PARS, the excitation wavelength will be chosen to be twice the conventional value. Two photons will then be absorbed simultaneously, providing an excitation event equivalent to standard single photon excitation (fig. 19). In the example listed above, 532nm excitation may be used to target DNA absorbance, rather than using 266nm UV excitation. The two-photon 532nm absorption corresponds to a single 266nm absorption. Aspects disclosed herein are not limited to 532nm excitation. The wavelength of excitation may be configured to be twice the predetermined excitation wavelength, such as twice the UV wavelength (e.g., twice 100-400 nm) or the UVC wavelength (100-280 nm).
The main difference between multiphoton PARS and traditional single photon PARS architectures is the requirement for high instantaneous optical energy density. To reduce the sample exposure level to a practical level, this architecture may require the use of very short optical excitation pulses, on the order of a picosecond or less. This requirement may be unique to multiphoton PARS.
Multiphoton PARS may provide several benefits over conventional PARS excitation. First, multiphoton excitation uses photons of longer wavelength, which have lower energy and penetrate deeper. Second, shifting to longer wavelengths may provide further biocompatibility, thereby avoiding tissue damage. In the case of in situ histology, this is particularly common because PARS UV excitation may be incompatible with imaging deep into the body. It may also improve the security of the PARS system for field applications.
TD-PARS and feature extraction imaging
PARS works by capturing nanosecond-scale light perturbations generated by photoacoustic pressure. These time-domain (TD) modulations are typically projected through an amplitude to determine the absorption amplitude. A single characteristic intensity value may be extracted from each TD signal to visualize the total absorption amplitude at each point. For example, the TD amplitude, calculated as the difference between the maximum and minimum of the TD signal, is typically used to represent the absorption amplitude.
However, important information about the material properties of the target is contained within the TD signal. The time evolution of the PARS signal may be determined by material properties such as density, heat capacity, and acoustic impedance. By applying an AI algorithm that bypasses the PARS image reconstruction step, an H & E-like visualization can be generated directly from the PARS temporal data. This approach is advantageous over direct PARS-to-H & E image-to-image conversion because it provides additional valuable information that can help better distinguish between different tissue types in an image.
Referring to fig. 20A and 20B, an H & E-like representation may be made by generating AI image-to-image conversion algorithms for the countermeasure network (conditional generative adversarial network, cGAN) based on conditions. These methods learn a color transfer map from paired or unpaired samples of the source representation and the reference representation. In this way, the reconstructed gray scale PARS image (20A) can be mapped into color H & E data (20B).
The imaging modality may scan pixel by pixel, capturing signals at each pixel over time. While the scanning may be continuous over time, in practice the signal is recorded periodically or discretely using an image acquisition system. The eigenvalues may be extracted from each signal by using the hilbert transform to find the envelope of the signal from which the difference between the maximum and minimum values can be calculated, or by directly calculating the difference between the maximum and minimum values of the original signal itself.
Referring to fig. 21A and 21B, the methods and techniques disclosed herein may bypass an image reconstruction stage in which images are reconstructed by extracting the magnitudes of captured optical absorption signals or averaging their values over time. The methods and techniques disclosed herein may directly use the signal representation as an input to the AI colorization algorithm, rather than reconstructing the pixels of the image. In this way, additional valuable information about the internal organization may be included to create a virtual H & E-like image.
In order to make the colorization algorithm more computationally efficient, some compressed representation of the time domain signal may be used. For example, these may include, but are not limited to: the dominant component of the signal, coefficients of other signal decomposition methods, salient signal points, etc. An example of creating an H & E-like visualization by applying the Pix2Pix algorithm is shown in fig. 21. Fig. 21A shows three principal components of the time domain signal, and fig. 21B shows a corresponding synthesized H & E image. The differences between fig. 20A-20B and fig. 21A-21B may not be apparent in black and white, and may be better assessed in color form. For example, fig. 21A may display some colors, while fig. 20A may be black and white and/or gray. Further, fig. 21B may be finer and/or display more colors than fig. 20B.
Intelligent clustering method
An unsupervised clustering method can be used to form colored composite H & E images without the need to reconstruct gray scale images. The clustering method may learn TD features associated with potential biomolecular features. The technology identifies the characteristics related to the composition biomolecules and realizes single-acquisition virtual tissue marking. A color visualization of the tissue is produced highlighting specific tissue components. Clustering may be performed on any or all of the PARS radiation, non-radiation, and scatter channels.
For a given biomolecule with constant material properties, the PARS TD signal may have a specific shape. However, the signal from a given target may vary in amplitude (e.g., based on concentration) and may be affected by noise. Clustering the signals by shape and learning a correlation prototype for each cluster can be used to determine constituent time domain features that capture material specific information of internal tissue targets, independent of noise and amplitude variations present in the TD signal.
As an example, a modified K-means clustering method may be used. The measured signal is taken as a vector, where the vector angle resembles the signal shape. The distance or difference between TD signals is a sine of the diagonal such that the quadrature signal has the greatest distance and the scaled or inverted signal has zero distance. Then, the cluster center is calculated as the union of each cluster and its negative union first principal component, thereby making the learned center robust to noise. Once the TD feature (center) is learned, the corresponding feature amplitude is extracted by performing a basic change from the time domain to the feature domain.
Referring to fig. 22, fig. 22 illustrates an exemplary architecture 2200 that can be targeted with a single (only one or exactly one) excitation such that several biomolecules, such as collagen, elastin, myelin, DNA, and RNA, are targeted to absorb mainly UV excitation (e.g., 266 nm). Subsequently, clustering methods can be used to create enhanced absorption contrast visualizations and extract biomolecule-specific features from the TD signals. UV excitation may be provided by an excitation light source 2202, such as a 50khz 266nm laser (e.g., WEDGE XF 266,Bright Solutions). Excitation may be spectrally filtered with prism 2204 and then expanded (e.g., with a variable beam expander or VBE 2206) prior to combining with the detection beam. The excitation light may be directed via one or more mirrors 2208.
The detection light may be provided by a detection light source 2212, such as a continuous wave 405nm OBIS LS laser. The detection may be fiber coupled through a circulator 2214, collimated (e.g., using a collimator 2216), and then combined with the excitation beam via a dichroic mirror 2210. The detection light may be directed via one or more mirrors 2218.
The combined excitation and detection may pass through a pair of alignment mirrors 2200 and be confocal onto the sample through a UV transparent window. The back reflected light from the sample may return to collimator 2216 and circulator 2214 through the same path as the forward propagation. The circulator 2214 may redirect the backscattered light to the capture nanosecond intensity modulated photodiode 2222. During image acquisition, stage 2226 may raster scan the sample over the objective lens while the excitation pulses are continuous. The analog photodiode output may be captured for each firing event using a high-speed digitizer to form a PARS TD signal. Each PARS TD may then be mapped to a pixel in the final image using the stage position signal, which may be output on an electronic display and/or computer 2228.
Referring to fig. 23 to 24, the proposed K-means method may utilize TD features according to the number of extracted clusters, instead of defining pixel values by TD signal amplitudes. If only a single feature is requested (k=1), the clustering algorithm produces features that contain TD shapes that are similar to all tissue components. This feature can then be used as a basis for matched filtering designed as a technique to optimally extract the amplitude of the known signal shape with added noise. This provides a robust anti-noise method for determining absorption amplitude or pixel "brightness". As shown in view (a) of fig. 24, this extraction is applied in tissue, providing a significant improvement in structural image quality and noise suppression compared to conventional TD amplitude projection.
If additional clusters (K > 1) are requested, the tissue-specific temporal features are learned. In this case, the feature amplitude at each pixel is extracted by performing a basic change from the time domain to the feature domain. To intuitively illustrate the efficacy of learning features, the time domain signals are clustered for k=2 requested features. By projecting the high-dimensional time domain data onto a two-dimensional plane containing the learned features, the TD signals (points) relative to the identified features (arrows) can be visualized. In the visualization, each point is colored in proportion to the signal content due to the constituent features.
Three features (k=3) were used to generate further visualizations for resected murine brain tissue. The extracted feature magnitudes are mapped to separate red, green and blue (R, G, B) color channels to form a color visualization. The pixel color therefore represents a proportional mix of the contributions of each feature to the time signal, while the intensity represents the total amplitude of the absorbed energy. Referring to view (c) of fig. 24, staining of k=3 demonstrates the potential of the proposed technique in restoring biomolecule-specific information. The structure of individual marrow neurons (white matter) from the brain stem is shown in pink, projected into the brain. Meanwhile, the non-medullary neurons (gray matter) appear in green on the right side of the picture. Finally, the nuclear structures dispersed throughout the brain tissue appear white.
Referring to fig. 25, three different regions of brain tissue (view (a)), gray matter (view (c)), and transition or boundary between white and gray matter (view (b)) are selected based on macroscopic examination. Each unique region was imaged with a PARS microscope prior to staining using the same k=3 model. In each selected region, the TD coloration highlights those biomolecule-specific structures that are identical to those recognized in the initial colored image (fig. 24 (c)).
TD signals may be clustered by shape rather than by amplitude. A given pixel (and its corresponding TD signal) may be expressed in terms of the characteristic signal shape and residual terms of one or more targets. Specifically, for a given signal s and learned characteristic signal shape (characteristic) { f i The signal may be represented as s= Σ i α i f i So that the weight { alpha } i The ratio of each characteristic signal shape is specified, including the residual term r, to encapsulate any errors due to modeling or measurement noise.
The TD signal may be a vector in space Rn, where the dimension n of space is simply the number of discrete TD samples. Because the TD signal is considered a cartesian vector, the signal shape is similar to a vector angle. A unit vector pointing in the direction of the non-noise portion of a given cluster may define a center. The union may be constructed from the clusters and their inverse, and the center may be the direction of maximum variance (the principal component from the sample covariance), allowing higher amplitude signals to have the greatest effect.
The aggregation algorithm is reflected in FIG. 26, and the corresponding method 2700 is reflected in FIG. 27. The computation of the cluster center is reflected in line 16, and singular value decomposition (Singular Value Decomposition, SVD) can be used to extract the first principal component. For input, the clustering algorithm uses a set of PARS TD signals s= { S i (t) } and the number of clusters requested K (same as the number of learned features). Furthermore, the convergence criterion is specified by the minimum number of mobile criteria and the difference of the average residual criterion. These are needed to ensure convergence.
The algorithm may run several times and may return the optimal solution only (in terms of minimum average residual). The algorithm is initialized by randomly selecting K TD signals as initial cluster centers, as shown in lines 1-3 and step 2702. Next is a "membership update" step, as shown on lines 7-12 and steps 2704 and 2706, wherein the cluster membership of all points (PARS TD signal) is updated by evaluating the distance from each point to each center in step 2704 and assigning members to related clusters from the smallest center in step 2706. The number of points moved (change cluster membership) is recorded (lines 9-11). Next, in step 2708, the average residual (line 13) and the variation of the average residual (line 14) from the previous iteration, starting from zero in the case of the first iteration, are evaluated. Next is a "center update" step shown on lines 16-21 and in step 2710, where the center is updated and calculated as the first principal component of the union of each cluster and its negative union. In practice, this is calculated by Singular Value Decomposition (SVD), as shown in line 19. In step 2712, the centers are normalized so that they are unit sizes. Finally, in step 2714, convergence criteria are checked. If the algorithm has not converged (no in fig. 27), the "member update" step is repeated, followed by the "center update" step, until the convergence criterion is met (yes in fig. 27). At step 2716, the algorithm returns as output a set of cluster labels (which indicates which cluster each PARS TD signal is associated with) and a set of K cluster centers as learned time-domain features.
The PARS TD signal may contain sufficient information to identify biomolecules based on their clustered TD characteristics. These properties can be transferred across images of different tissue samples. Feature recognition may be performed on the initial sample and then transferred to other samples, producing similar convincing results. Furthermore, this technique provides a unique advantage in that the clustering method does not require a priori information except for the number of clusters. Training may be performed blindly on signals captured within the sample of interest. This is especially beneficial in complex samples (e.g., resected brain tissue as explored herein). The challenge is that blind clustering of a preselected number of features does not guarantee that each feature can separate out a single biomolecule/tissue type. Each cluster is directed only to a unique characteristic of the PARS TD signal, which can be used to highlight different tissue components.
Biomolecules can be visualized based on their PARS TD characteristics. This approach can enable a single (only one or exactly one) widely absorbed excitation source to provide material specificity that is otherwise difficult to obtain, while targeting the optical absorption of several biomolecules. This may enable enhanced absorption contrast visualization to be obtained in a short time compared to a similar multi-wavelength method. This greatly expands the potential for biomolecule specificity by adding an additional dimension to absorption contrast, expanding several new approaches for label-free PARS microscopy.
Other methods
Referring to fig. 28, an additional method of extracting signals is also contemplated, aimed at providing excellent PARS non-radiative signal extraction. As previously described with reference to fig. 17, the average value of the regions before and after modulation can be used as a method of noise reduction. However, additional extensions of this concept may provide improved performance in more challenging scenarios. In particular, when the interrogation point moves rapidly over the sample surface, it may be subject to additional non-PARS-based modulation due to spatial variations around the sample. In these cases, additional steps may be required to estimate the non-modulated scatter. If the method described with respect to FIG. 17 can be referred to as a "step" process, a similar "angled step" process can be envisaged. Here, the non-modulated scattering may be approximated by using an average of the pre-modulation region and the post-modulation region from which PARS amplitude and time domain information may be extracted. Finer methods, such as partial curve fitting of specific pre-and post-modulations, are also contemplated for the same final objective.
Referring to fig. 29, additional information may also be provided by recording various analog filtered instances of a single (only one or exactly one) PARS signal. For example, a relatively unfiltered signal as well as a high bandpass signal may be obtained by separating the original analog signal from the photodetector and recording it on two separate channels. Thus, intelligent methods (such as the K-means method described above) can be used independently for the filtering iterations of the various records. Because these represent highly independent signal measurements, additional signal fidelity can be extracted from such a process, thereby improving sensitivity.
Referring to fig. 30, additional information may also be provided by utilizing the expected spatial correlation between adjacent points. For example, the data volume may be reconstructed using two conventional transverse image axes and a third axis containing each respective time domain. This may facilitate a transversal processing operation prior to time domain signal extraction. Here, the interdependence and the interdependence along the horizontal axis and the time axis can be utilized to approximate a center signal with significantly lower noise. Similar non-intelligent methods may be performed in any or all of the PARS radiation, non-radiation, and scattering channels.
(from radiation, non-radiation and scattering)Functional extraction
As explained previously with respect to QER, characteristics such as thermal diffusivity, conductivity, and sound velocity may determine the PARS relaxation time. Features such as those related to temperature, sound velocity, and molecular information can be extracted from the time domain signal. As an example, two targets may have the same or similar optical absorption, but other characteristics are slightly different (such as different sound speeds), which may result in different attenuation, evolution, and/or shape of the signal. The attenuation, evolution and/or shape of the signal can be used to determine new molecular information or add it to the PARS image.
Various optical and mechanical properties may lead to these differences in signal shape. For example, the rate at which the signal returns to the backscatter level may be determined by the local thermal diffusivity. Thus, for example, a region with a higher thermal diffusivity may feature a shorter signal length than a region with a lower thermal diffusivity. This can be used to distinguish between nuclei and surrounding areas with similar optical absorption. Also, signal lifetime may be affected by local sound velocity. One example may be used to distinguish between two different metals. Aluminum and copper will have different thermal diffusivity and sonic characteristics to facilitate multiplexing by measuring signal lifetime alone. Fig. 31 illustrates two signals with different lifetimes.
Post imaging correction
Referring to fig. 32, by acquiring two (or more) unique absorption-based measurements (radiation and non-radiation), local variations in these acquisitions can be used to compensate for excitation pulse energy variations. For example, similar local (pixel level) changes of two acquisitions may be compared for similar local (pixel level) changes of close or sub-resolution in spacing, which changes are close or below resolution in spacing. Rapid local changes may not be due to spatial variations in the sample, as it is not expected that the system will provide such a level of spatial resolution. Thus, similar variations may be interpreted as similar reconstruction errors between the two visualizations. This interpretation can then be used to provide post-imaging intensity correction, thereby providing additional qualitative recovery. Although fig. 32 shows an example of autofluorescence-based compensation, aspects disclosed herein are not limited to autofluorescence and other absorption-based measurements may be used.
Chirped pulse PARS acquisition
Referring to fig. 33, the practical bandwidth and noise limitations in such devices may provide significant impediments to high speed in view of the fact that PARS acquisition is typically performed by capturing time-varying sample responses using a single photodetector element. One possible solution to this may be stripe detection of the PARS signal. Fringe detection involves spatially separating the various temporal components across several detectors (such as those in a line scan or standard camera), which can be accomplished by several methods.
For example, chirped pulses (pulses having varying wavelengths along the pulse length) may be used for detection, and one or more diffractive or dispersive elements (such as prisms or gratings) may be used to spatially separate the various wavelength components that may now encode time information. This process can significantly improve time resolution while maintaining high signal fidelity by extending detection over a large number of detectors. Such an architecture would have explicit utility, such as in combination with a line scan architecture, where detection is performed on a large array such as a camera, where two spatial coordinates of the camera encode one spatial dimension and one temporal dimension from the sample. Other methods of scribing across the sensor array along the time axis are also contemplated, such as using a high speed optical scanner.
Acquisition of time domain PARS with integrated photodetector unit
Many imaging sensors have a minimum integration time, which may not capture nanosecond modulations in the evolution of the PARS signal. This may be a limitation due to the potentially rich time domain information provided in the PARS signal. We propose a generic strategy with rolling shutter/trigger sequence/delay dropping that will capture the modulation during the integration time of these photosensors.
In such a PARS collection mechanism, the back-scattered detection light carrying the PARS modulation may be distributed across the array of integrated light detection units. At the beginning of acquisition, a tunable delay may be introduced between the integration start times of each light detection unit (e.g., by using a rolling shutter, a predetermined trigger sequence, delaying dropping and/or capturing differently timed portions of the recovery signal). If the delay time is shorter than the light detection unit integration time, a signal having a time resolution defined by the applied delay can be reconstructed. For example, PARS time domain information may be extracted by taking the derivatives of these time interval integration windows and/or by analyzing their common regions at the time of rendering. A visual depiction of this acquisition method is shown in fig. 34. For example, instead of a high sample rate photodetector, a CCD/CMOS camera sensor may be utilized to resolve the time domain signal. In this case, the row of the CCD/CMOS camera is a light detection unit that captures a signal in a rolling shutter manner. By applying a delay between the individual photodetector lines, a PARS time domain signal can be constructed that has a greater time resolution than a single integral sensor.
Data compression
Referring to fig. 35, digital and/or analog techniques may be used to compress the data. For example, using the K-means approach, the original time domain signals may be suitably represented by their respective K-means weights. For example, if three such prototypes were used on a particular data set, rather than storing the complete time domain (about 200 samples), the time axis could be well compressed to simply three values or floating point values. Similar such extracted features may be used instead of the complete uncompressed time domain for purposes of reduced system RAM usage, reduced data bandwidth requirements, reduced system memory load, etc.
Quick acquisition method
Acquisition at higher interrogation rates may require a finer acquisition process. Various problems may occur when interrogating a sample at a higher acquisition rate, including logistical movement of the interrogation spot around the sample and higher frequency optical scattering signals. Rapid lateral movement of the interrogation spot around the sample may be achieved by a hybrid scanning method that combines a fast optical scanning method (e.g., resonant scanner and polygon scanner) with a bulk scanning method (e.g., mechanical scanning stage). Such methods in other optical microscopy methods have facilitated interrogation rates of 10 megahertz and may provide similar benefits as PARS mode.
However, such rapid movement of the interrogation spot around the sample may also cause additional undesirable scattering frequency content, which may confound the time-domain signal processing of the collected PARS signal. Thus, as shown in FIG. 36, it may be beneficial to manipulate the detection focus spot on the sample at a larger size than the excitation spot, so that the excitation spot may be scanned around a relatively stationary or slower moving detection spot, thereby reducing the impact of the fast optical scan of the detection.
Data coloring
Referring to fig. 37, the techniques and methods disclosed herein may allow for the direct construction of colored color H & E analog images bypassing gray scale or scalar-amplitude based reconstruction. The colors used may simulate various hues traditionally used in H & E staining, such as pink, violet, and/or blue. However, aspects disclosed herein are not limited to pink, purple, and/or blue, and the system and processor may be configured to use other colors. For example, red, green, and blue channels may be used to represent three extracted K-means prototypes.
Augmented reality interface
After completion of the processing of the data visualizations or images, these visualizations may be displayed on the user interface screen in combination with other visualizations and/or overlaying other visualizations on the user interface screen. For example, a low resolution bright field image of a sample may form the background of a PARS visualization presented. Such enhancements may be used to help maintain the orientation between the desired visualization and the original sample.
Application of
Aspects disclosed herein may include non-radiative (heat and pressure) and radiative (fluorescence is one of the possible signals) signals in the sample. Aspects disclosed herein may include collecting radiative and non-radiative relaxation due to optical absorption and scattering from both excitation and detection. The collected signals and/or raw data may be used to directly image and stain a sample, such as an H & E (hematoxylin and eosin) histological image, without staining the sample. H & E histological images can be directly formed and stained using the methods disclosed herein (e.g., based on a comparison of non-radiative and radiative signals, QER, lifetime or evolution of signals, and/or clustering algorithms) and using features in the original PARS signals. Aspects disclosed herein may be used to determine or measure mechanical properties of a sample, such as sound velocity and/or temperature properties, using a light absorbing remote sensing system or PARS. A small or precisely located region of the sample (e.g., a focused laser beam or beam size) may be used to measure these features or characteristics. Aspects disclosed herein may not only extract the amplitude or scalar amplitude of a signal in a sample. For example, two targets may have the same or similar optical absorption but slightly different other characteristics (such as different sound speeds), which may result in different evolutions and/or shapes of the signals. Aspects disclosed herein may be used to determine new molecular information or to add it to the PARS image.
It is apparent that other examples can be designed with different fiber-based or free-space components to achieve similar results. Other alternatives may include various coherence length sources, use of balanced photodetectors, interrogation beam modulation, the addition of optical amplifiers in the return signal path, and the like.
No reagents or ultrasound coupling medium are required during in vivo imaging experiments. However, prior to the non-contact imaging session, the target may be prepared with water or any liquid (e.g., oil). Also, in some cases, an intermediate window, such as a cover slip or glass window, may be placed between the imaging system and the sample.
Aspects disclosed herein may use a combination of a PARS device and optical coherence tomography (optical coherence tomography, OCT). OCT is a complementary imaging modality to PARS devices. OCT measurements may be performed using various methods, such as in time domain optical coherence tomography (time domain optical coherence tomography, TD-OCT) or in frequency domain optical coherence tomography (frequency domain optical coherence tomography, FD-OCT) as described in US2010/0265511 and US 2014/0125952. In OCT systems, multiple a-scans are typically acquired while scanning a sample beam laterally across a tissue surface, creating a two-dimensional map of reflectivity versus depth and lateral extent, commonly referred to as B-scan. The lateral resolution of the B-scan is approximated by the confocal resolution of the sample arm optics, which is typically given by the size of the focused spot in the tissue.
All light sources, including but not limited to PARS excitation, PARS detection, PARS signal enhancement, and OCT sources, can be implemented as continuous beams, modulated continuous beams, or short pulse lasers with pulse widths ranging from attoseconds to milliseconds. These may be set to any wavelength suitable for exploiting the optical (or other electromagnetic) properties of the sample, such as scattering and absorption. The wavelength may also be selected to purposefully enhance or suppress detection or excitation photons from different absorbers. Wavelengths may range from nanometers to micrometers. The continuous beam power may be set to any suitable power range, for example from attorney to watt. The pulse source may use pulse energy suitable for the particular sample being tested, such as in the range from afocal to joule. Various coherence lengths can be implemented to take advantage of interference effects. These coherence lengths can range from nanometers to kilometers. Likewise, the pulse source may use any repetition frequency deemed suitable for the sample under test, such as from continuous waves to gigahertz range. The source may be tunable, monochromatic or polychromatic.
The TA-PARS, MP-PARS, multiphoton excitation PARS, QER, lifetime PARS, and TD-PARS subsystems may include interferometers such as michelson interferometers, fizeau interferometers, lambda-cyber interferometers, fabry-perot interferometers, mach-zehnder interferometers, or optical quadrature detection. The interferometer may be free space or fiber-based or some combination. The basic principle is that interferometry can be used to detect phase and amplitude oscillations in the probe receiver beam and various detectors can be used to detect at AC, RF or ultrasound frequencies.
The TA-PARS, MP-PARS, multiphoton excitation PARS, QER, lifetime PARS, and TD-PARS subsystems may use and implement non-interferometric detection designs to detect amplitude modulation within the signal. The non-interferometric detection system may be free space or based on optical fibers or some combination thereof.
The TA-PARS, MP-PARS, multi-photon excitation PARS, QER, lifetime PARS, and TD-PARS subsystems may use various optical fibers such as photonic crystal fibers, image guided fibers, double clad fibers, and the like.
The PARS subsystem may be implemented as a conventional photoacoustic remote sensing system, non-interfering photoacoustic remote sensing (non-interferometric photoacoustic remote sensing, NI-PARS), camera-based photoacoustic remote sensing (camera-basedphotoacoustic remote sensing, C-PARS), coherent gating photoacoustic remote sensing (sphere-gated photoacoustic remote sensing, CG-PARS), single-source photoacoustic remote sensing (SS-PARS), or an extension thereof.
In one example, all light beams may be combined and scanned. In this way, PARS excitations can be sensed where they are generated in the same area and where they are largest. OCT detection may also be performed at the same location as PARS to aid registration. Other arrangements may also be used, including keeping one or more beams fixed while other beams are scanned, and vice versa. The optical scanning may be performed by galvanometer mirrors, microelectromechanical system (Micro-Electro-Mechanical System, MEMS) mirrors, polygon scanners, stepper/dc motors, etc. The mechanical scanning of the sample may be performed by a stepper stage, a dc motor stage, a linear drive stage, a piezoelectric stage, etc.
Both optical scanning and mechanical scanning can be used to scan a sample in one, two or three dimensions. Adaptive optics such as an adjustable acoustic gradient (tunable acoustic gradient, TAG) lens and deformable mirror may be used to perform axial scanning within the sample. Both optical and mechanical scanning may be combined to form a hybrid scanner. The hybrid scanner may employ single axis or dual axis optical scanning to capture large areas or swaths in a short period of time. Custom control hardware can be used to potentially control the mirrors to have custom scan patterns to improve scan efficiency in terms of speed and quality. For example, an optical axis may be used to rapidly scan while a mechanical axis is used to move the sample. This may present a ramp-like scan pattern that may be interpolated. Another example of using custom control hardware is stepping the mechanical stage only when the fast axis has completed moving to produce a cartesian-like grid that may not require any interpolation.
PARS may provide (three dimensional, 3D) imaging by optical or mechanical scanning of the beam or mechanical scanning of the sample or imaging head or a combination of mechanical and optical scanning of the beam, optics and sample. This may allow for fast structural and functional cross-section or 3D imaging.
One or more pinholes may be employed to block out-of-focus light when optically or mechanically scanning a light beam or a mechanical scan of a sample or imaging head, or a combination of mechanical and optical scans of a light beam, optics, and sample. They can improve the signal-to-noise ratio of the resulting image.
The beam combiner may be implemented using dichroic mirrors, prisms, beam splitters, polarizing beam splitters, wavelength division multiplexing (Wavelength Division Multiplexing, WDM), etc.
The beam path may be focused on the sample using different optical paths. Each of the single or multiple PARS excitation, detection, signal enhancement, etc. paths and OCT paths may use separate focusing elements on the sample, or all paths share a single (only one or exactly one) path, or any combination. The beam paths may be returned from the sample using unique optical paths that are different from those used to focus to the sample. These unique optical paths may interact with the sample at normal incidence or may interact at an angle, for example, the central beam axis forms an angle of 5 to 90 degrees with the sample surface.
For some applications, such as in ophthalmic imaging, the imaging head may not employ any primary focusing element, such as an objective lens, to tightly focus light onto the sample. Instead, the beam may be collimated or loosely focused (so as to produce a spot size much larger than the diffraction limit of light) and directed onto the sample. For example, ophthalmic imaging devices direct a collimated beam of light into an eye, allowing the lens of the eye to focus the beam onto the retina.
The imaging head may focus the light beam to a depth of at least 50nm in the sample. The imaging head may focus the beam into the sample to a depth of at most 10mm. The increased depth compared to previous PARS results from the novel use of deep penetration detection wavelengths as described above.
The light may be amplified by an optical amplifier before interacting with the sample or before detection. Light may be collected by photodiodes, avalanche photodiodes, phototubes, photomultiplier tubes, CMOS cameras, CCD cameras (including EM-CCDs, enhanced CCDs, back-thinned and cooled CCDs), spectrometers, and the like. The detected signal may be amplified by a Radio Frequency (RF) amplifier, a lock-in amplifier, a transimpedance amplifier, or other amplifier configuration.
The modality may be used for A, B or C-scan images for in vivo, ex vivo or phantom studies. The TA-PARS, MP-PARS, multiphoton excitation PARS, QER, lifetime PARS and TD-PARS subsystems may take the form of any of the usual embodiments of microbiological imaging techniques. Some of these may include, but are not limited to, devices implemented as a bench-top microscope, an inverted microscope, a hand-held microscope, a surgical microscope, an endoscope, or an ophthalmic device, among others. These may be constructed based on principles known in the art.
The TA-PARS, MP-PARS, multiphoton excitation PARS, QER, lifetime PARS, and TD-PARS subsystems may be optimized to increase the depth of focus of two-dimensional (2D) and 3D imaging with a multifocal design. Chromatic aberration in the collimator lens and objective lens pair may be used to refocus the light from the fiber into the object such that each wavelength is focused at a slightly different depth position. These chromatic differences can be used to encode depth information into a recovered PARS signal that can later be recovered using wavelength specific analysis methods. The use of these wavelengths together may also be used to improve the depth of field and Signal-to-Noise Ratio (SNR) of the PARS image. During imaging, a depth scan through wavelength tuning may be performed.
The PARS method can distinguish samples laterally or axially by spatially encoding the detection region (such as by using several pinholes) or by spectral content of the broadband beam.
The TA-PARS, MP-PARS, multiphoton excitation PARS, QER, lifetime PARS, and TD-PARS subsystems may be combined with other imaging modes (such as stimulated raman microscopy, fluorescence microscopy, two-photon and confocal fluorescence microscopy, coherence-anti-raman-stokes microscopy, raman microscopy, other photoacoustic and ultrasound systems, etc.). This can allow simultaneous imaging of microcirculation, blood oxygenation parameters, and other molecular specific targets, a potentially important task that is difficult to achieve. A multi-wavelength visible laser source may also be implemented to generate light absorption signals for functional or structural imaging.
The polarization analyzer may be used to decompose the detection light into respective polarization states. The light detected in each polarization state may provide information about the sample. The phase analyzer may be used to decompose the detected light into phase components. This may provide information about the sample.
The TA-PARS, MP-PARS, multiphoton excitation PARS, QER, lifetime PARS and TD-PARS subsystems can detect the generated signal in the detection beam returned from the sample. These disturbances may include, but are not limited to, variations in intensity, polarization, frequency, phase, absorption, nonlinear scattering, and nonlinear absorption, and may be caused by various factors such as pressure, thermal effects, and the like.
Analog-based signal extraction may be performed along the electrical signal path. Some examples of such analog devices may include, but are not limited to, lock-in amplifiers, peak detection circuits, and the like.
The PARS subsystem may detect time information encoded in the back-reflected detection beam. This information can be used to distinguish chromophores, enhance contrast, improve signal extraction, etc. Analog and digital processing techniques may be used to extract this time information. These may include, but are not limited to, the use of lock-in amplifiers, fourier transforms, wavelet transforms, intelligent algorithm extraction, and the like. In one example, a phase lock detection may be utilized to extract a PARS signal similar to the known expected signal for extracting a specific chromophore (e.g., deoxyribonucleic acid (DeoxyriboNucleic Acid, DNA), cytochromes, red blood cells, etc.).
The imaging head of the system may include closed-loop or open-loop adaptive optics including, but not limited to, wavefront sensors, deformable mirrors, TAG lenses, etc., for wavefront and aberration correction. Aberrations may include defocus, astigmatism, coma, distortion, third order effects, and the like. The signal enhancing beam may also be used to suppress signals from undesired chromophores by purposefully inducing saturation effects such as photobleaching.
Various types of optics may be utilized to their respective advantages. For example, the axicon may be used as a primary objective lens to produce a Bessel beam with a greater depth of focus than can be provided by standard Gaussian beam optics. Such optics may also be used at other locations within the beam path, as appropriate. Reflective optics may also be substituted for their respective refractive elements, such as the use of a reflective objective lens instead of a standard compound objective lens.
The optical path may include nonlinear optical elements for various related purposes, such as wavelength generation and wavelength shifting. The beam foci may overlap at the sample, but may also be offset laterally and axially from each other by a small amount, as appropriate.
TA-PARS, MP-PARS, multiphoton excitation PARS, QER, lifetime PARS, and TD-PARS subsystems may be used as spectrometers for sample analysis.
Other advantages inherent to this structure will be apparent to those of ordinary skill in the art. The embodiments described herein are illustrative and are not intended to limit the scope of the claims, which are to be construed in accordance with the entire specification.
Application of
It will be appreciated that the systems described herein may be used in various ways, such as those described in the prior art, and may also be used in other ways to utilize the aspects described above. A non-exhaustive list of applications is discussed below.
The system can be used to image angiogenesis in different preclinical tumor models.
By utilizing different wavelengths, different pulse widths, different coherence lengths, repetition rates, exposure times, different evolutions or lifetimes of the signals, quantum efficiency ratios, and/or other comparisons of non-radiative signals to radiative signals, etc., the system can be used to unmixe the target (e.g., detect, separate, or otherwise discrete constituent species and/or sub-species) based on the absorption, scattering, or frequency content of the target.
The system may be used to image at resolutions up to and exceeding diffraction limits.
The system can be used to image anything that absorbs light, including exogenous and endogenous targets and biomarkers.
The system may have some surgical applications such as imaging of functions and structures during brain surgery, evaluation of internal bleeding and cauterization verification, imaging of perfusion sufficiency of organs and organ transplants, imaging of angiogenesis around islet transplants, imaging of skin grafts, imaging of tissue scaffolds and biological materials for assessing angiogenesis and immune rejection, imaging of assisted microsurgery, guidance to avoid cutting critical blood vessels and nerves.
The system may also have some gastroenteropathy applications such as imaging vascular beds and depth of invasion in barrett's esophagus and colorectal cancer. In at least some embodiments, the depth of invasion is critical to prognosis and metabolic potential. This can be used for virtual biopsy, crohn's disease, irritable bowel syndrome (Irritable Bowel Syndrome, IBS) monitoring, carotid examination. Gastroenteropathy applications can be combined or onboard with clinical endoscopes and miniaturized PARS systems can be designed as stand-alone endoscopes or installed within accessory channels of clinical endoscopes.
The system can also be used for clinical imaging of microcirculatory and macrocycles, and pigment cells, which can be used for the following applications: such as (1) the eye, possibly enhancing or replacing fluorescein angiography; (2) Imaging skin lesions (including melanoma, basal cell carcinoma, hemangioma, psoriasis, eczema, dermatitis), imaging morse surgery, imaging verification of tumor margin excision; (3) peripheral vascular disease; (4) diabetic and pressure ulcers; (5) burn imaging; (6) plastic surgery and microsurgery; (7) Imaging of circulating tumor cells, in particular melanoma cells; (8) imaging lymph node angiogenesis; (9) Imaging a response comprising photodynamic therapy with a vascular ablation mechanism; (10) Imaging a response to chemotherapy comprising an anti-angiogenic drug; (11) imaging the response to radiation therapy.
The system may also be used for some histopathological imaging applications such as cryopathology, creating H & E-like images from tissue samples, virtual biopsies, etc. It can be used in a variety of tissue preparations such as formalin-fixed paraffin-embedded tissue blocks, formalin-fixed paraffin-embedded tissue slices, cryopathological sections, freshly resected samples, etc. In these samples, visualization of macromolecules (e.g., DNA, ribonucleic acid (RibonucleicAcid, RNA), cytochromes, lipids, proteins, etc.) can be performed.
The system may be used to estimate oxygen saturation using multi-wavelength PARS excitation in the following applications: (1) Venous oxygen saturation is estimated in cases where pulse oximetry cannot be used, including estimating cerebral venous oxygen saturation and central venous oxygen saturation. This may replace catheterization, which may be risky, especially for children and infants.
Oxygen flow and oxygen consumption may also be estimated by estimating oxygen saturation using PARS imaging and estimating blood flow in blood vessels flowing into and out of a tissue region.
The system can be used to isolate significant histological chromophores, such as the nucleus and surrounding cytoplasm, by utilizing their respective absorbance spectra.
These systems can be used to unmixe targets using their absorption content, scattering, phase, polarization or frequency content by using different wavelengths, different pulse widths, different coherence lengths, repetition rates, fluxes, exposure times, etc.
Other application examples may include: imaging of contrast agents in clinical or preclinical applications; identification of sentinel nodes; non-invasive or minimally invasive identification of tumors in lymph nodes; nondestructive testing of materials; imaging of gene-encoded reporter genes, such as tyrosinase, chromoprotein, fluorescent protein for preclinical or clinical molecular imaging applications; imaging an actively or passively targeted optically absorbing nanoparticle for molecular imaging; and imaging of thrombi and potential grading of the length of time that thrombi are present.
Other application examples may include: clinical and preclinical ophthalmic applications; oxygen saturation measurement and retinal metabolic rate, limbal vasculature and stem cell imaging, corneal nerve and neovascular imaging, schlemm's canal changes assessment in glaucoma patients, choroidal neovascularization imaging in diseases such as age-related macular degeneration, diabetic retinopathy and glaucoma; anterior and posterior segment blood flow imaging and blood flow status.
The system can be used to measure and estimate metabolism within biological samples using the capabilities of PARS and OCT. In this example, OCT may be used to estimate volumetric blood flow in the region of interest and the PARS system may be used to measure oxygen saturation in the vessel of interest. The combination of these measurements may then provide an estimate of the metabolism within the region.
The system can be used for head and neck cancer type and skin cancer type, functional brain activity, examining vascular system of stroke patients to help locate thrombus, monitoring changes in neuronal and brain function/development due to changes in intestinal bacterial composition, atherosclerotic plaques, monitoring oxygen sufficiency after flap reconstruction, abundance sufficiency after plastic or cosmetic surgery, and imaging cosmetic injections.
The system may be used for topology tracking of surface deformations. For example, OCT may be used to track the position of a sample surface. A mechanism such as adaptive optics may then be used to apply correction to the tightly focused PARS device to maintain alignment with the surface as the scan proceeds.
The system may be implemented in a variety of different forms suitable for these applications, such as a bench-top microscope, an inverted microscope, a hand-held microscope, a surgical microscope, an ophthalmic microscope, an endoscope, and the like.
Aspects disclosed herein may be used in the following applications: imaging a histological sample; imaging the nucleus; imaging the protein; imaging the DNA; imaging the RNA; imaging the lipid; imaging of blood oxygen saturation; imaging of tumor hypoxia; imaging of wound healing, burn diagnosis or surgery; imaging of the microcirculation; imaging blood oxygenation parameters; estimating blood flow in blood vessels flowing into and out of a tissue region; imaging of a molecular specific target; imaging angiogenesis in a preclinical tumor model; clinical imaging of microcirculatory and macrocirculatory pigment cells; imaging of the eye; fluorescein angiography is added or replaced; imaging a skin lesion; imaging melanoma; imaging basal cell carcinoma; imaging hemangiomas; imaging psoriasis; imaging eczema; imaging dermatitis; imaging the morse operation; imaging to verify tumor margin resection; imaging peripheral vascular disease; imaging diabetic ulcers and/or pressure ulcers; imaging burn; performing plastic surgery; microsurgery; imaging of circulating tumor cells; imaging melanoma cells; imaging lymph node angiogenesis; imaging a response to photodynamic therapy; imaging a response to photodynamic therapy with a vascular ablation mechanism; imaging the response to chemotherapy; imaging the frozen pathology sample; imaging paraffin-embedded tissue; imaging the H & E sample image; imaging oxygen metabolic changes; imaging the response to the anti-angiogenic drug; imaging the response to radiation therapy; estimating oxygen saturation using multi-wavelength PARS excitation; estimating venous oxygen saturation in the event that a pulse oximeter cannot be used; estimating cerebral venous oxygen saturation and/or central venous oxygen saturation; estimating oxygen flow and/or oxygen consumption; imaging vascular beds and depth of invasion in barrett's esophageal cancer and/or colorectal cancer; functional and structural imaging during brain surgery; assessment of internal bleeding and/or cauterization verification; imaging perfusion sufficiency of the organ and/or organ transplant; imaging angiogenesis around islet transplantation; imaging of skin grafts; imaging the tissue scaffold and/or biological material to assess angiogenesis and/or immune rejection; assisting in imaging of microsurgery; avoid the guidance of cutting vessels and/or nerves; imaging of contrast agents in clinical or preclinical applications; identification of sentinel nodes; non-invasive or minimally invasive identification of tumors in lymph nodes; nondestructive testing of materials; imaging of a gene-encoded reporter gene, wherein the gene-encoded reporter gene comprises tyrosinase, chromoprotein, and/or fluorescent protein for preclinical or clinical molecular imaging applications; imaging an actively or passively targeted optically absorbing nanoparticle for molecular imaging; imaging of thrombus; classifying the existence time of the blood clot; remote or non-invasive intratumoral assessment of glucose concentration by detecting endogenous glucose uptake peaks; assessment of organoid growth; monitoring of developing embryos; evaluation of biofilm composition; assessing tooth decay; evaluation of non-living structures; evaluating the composition of the drawing to nondestructively confirm its authenticity; evaluating archaeological relics; manufacturing quality control; manufacturing quality is guaranteed; replacement catheterization; gastroenterology application; single excitation pulse imaging over the entire field of view; imaging tissue; cell imaging; imaging of scattered light from the surface of the object; imaging of changes caused by absorption of scattered light; or optically absorptive non-contact imaging.
Aspects disclosed herein may provide a computer-implemented method of visualizing features in a sample. The method may include: receiving one or more light absorbing remote sensing or system (PARS) signals; clustering the received one or more PARS signals using a clustering algorithm to determine a characteristic of the sample; and determining an image based on the clustered PARS signals. Alternatively or in addition, the method may comprise: determining a ratio of the non-radiated signal to the radiated signal; determining a value as a function of the non-radiated signal and the radiated signal; and/or comparing the non-radiated signals, and/or scattered signals; and determining an image including the color based on the determined ratio, value, and/or comparison.
The PARS signal may be collected by: generating a signal in the sample at an excitation location using an excitation beam, the excitation beam being focused below a surface of the sample; interrogating the sample with an interrogating beam directed at an excitation location of the sample, the interrogating beam being focused beneath the surface of the sample; and detecting a portion of the interrogation beam returned from the sample. Generating the signal may include: pressure, temperature, and fluorescence (and/or other radiated and/or non-radiated signals) are generated. The return portion of the interrogation beam may be indicative of the generated pressure and temperature signals. The PARS signal is further collected by detecting a fluorescent signal from the excitation site of the sample, while detecting the resulting pressure and temperature signals. The PARS signal may be further collected by redirecting a portion of the returned interrogation beam and detecting interactions with the sample.
The wavelength of the excitation beam may be configured such that the sample absorbs two or more photons simultaneously, wherein the sum of the energies of the two or more photons may be equal to a predetermined energy. The method may include collecting the PARS signal.
Clustering the received PARS signals may be based on shape. The method may not include: the reconstructed grayscale image is analyzed to determine an image. Clustering the received PARS signals may not be based on scalar magnitudes. The method may not include: the scalar magnitude is mapped or visualized. The PARS signal may be indicative of a temperature characteristic of the sample. The PARS signal may be indicative of the speed of sound in the sample. The PARS signal may be indicative of molecular information. The PARS signal may be indicative of a characteristic in the sample in an area having a size defined by the focused beam. Receiving the PARS signal may include receiving a time-domain (TD) signal.
The method may include: a cluster center is determined based on the clustered PARS signals. The determined cluster center may comprise a characteristic time domain signal. Receiving the PARS signal may comprise: a back-scattered intensity, a radiated signal, and a non-radiated relaxation time domain signal are received.
Receiving the PARS signal may comprise: a radiated PARS signal and a non-radiated PARS signal are received. The method may further comprise: ratios and/or values are determined based on the radiated PARS signal and the non-radiated PARS signal. The ratio and/or value may be plotted against quantum efficiency (quantum efficiency, QE) values. The method may include: image and/or biomolecular information is determined based on the ratio and/or value.
The method may include: the decay or evolution time is determined based on the received PARS signal. Determining the image may include: one or more colors are determined based on the clusters. The method may include: the image is displayed on a display.
The systems and techniques disclosed herein may provide a light absorption remote sensing (PARS) system for imaging features in a sample. The system may include an excitation light source configured to generate a signal in the sample at an excitation location, an interrogation light source, the excitation light source focused below a surface of the sample; the interrogation light source is configured to interrogate the sample and toward an excitation location of the sample, the interrogation light source being focused beneath a surface of the sample, a portion of the at least one interrogation light source returning from the sample being indicative of the generated signal; the processor is configured to execute a clustering algorithm to cluster the generated signals and to determine an image based on the clustered generated signals, the image being indicative of a feature in the sample. The system may include a display configured to display the determined image. The image may be formed directly from the received signal.
The processor may be configured to determine one or more colors based on the clusters. The determined color may include: purple, blue and pink, such that the image is configured to resemble hematoxylin and eosin (H & E) stained images.
The systems and techniques disclosed herein may provide a computer-implemented method of visualizing features in a sample. The method may include: the method includes receiving one or more signals, clustering the received signals based on shape using a clustering algorithm to determine temporal features of samples, and determining an image based on the clustered signals and the determined temporal features, the image including one or more colors used therein.
The method may include: a vector angle is determined from the received one or more signals. Clustering the received signals based on shape may include: the received signals are clustered based on vector angle. The one or more signals may include at least one of a non-radiated signal or a radiated signal. The one or more signals may include at least one of a non-radiative thermal signal or a non-radiative pressure signal. The one or more signals may include a radiative fluorescent signal. The radiative fluorescent signal may be a radiative autofluorescent signal. The non-radiative and radiative signals may include pressure signals, temperature signals, ultrasound signals, autofluorescent signals, nonlinear scattering, and/or nonlinear fluorescent signals.
Aspects disclosed herein may provide a computer-implemented method of visualizing features in a sample. The method may include: receiving a signal comprising a non-radiated signal and a radiated signal from the sample; clustering the received one or more signals using a clustering algorithm to determine a characteristic of the sample; and determining an image based on the cluster signal. The non-radiative signals may include thermal signals and pressure signals, and the radiative signals may include fluorescent signals. The entire non-radiative relaxation and radiative relaxation may be received, such as pressure signals, temperature signals, ultrasound signals, autofluorescence signals, nonlinear scattering, and nonlinear fluorescence.
At least some of the signals are collected by: the method includes generating a signal in the sample at an excitation location using an excitation beam, interrogating the sample with an interrogation beam directed toward the excitation location of the sample, and detecting a portion of the interrogation beam returned from the sample. By detecting optical absorption and scattering from the sample, at least a portion of the signal may be collected. Optical absorption and scattering may occur in the excitation and detection of the sample.
Aspects disclosed herein may provide a method of visualizing features in a sample. The method may include: receiving one or more signals; clustering the received signals using a clustering algorithm based on a shape, the shape based on a vector, to determine characteristics of the samples; and determining an image based on the cluster signal and the determined features, the image including one or more colors for use therein.
Claims (33)
1. A method of visualizing details in a sample, comprising:
generating a radiation signal and a non-radiation signal in the sample at an excitation location using an excitation beam, the excitation beam being focused below a surface of the sample;
interrogating the sample with an interrogating beam directed at an excitation location of the sample, the interrogating beam being focused beneath a surface of the sample; and
detecting light from the sample, the detected light comprising a portion of the interrogation beam returned from the sample, wherein the detected light is indicative of the generated radiation signal and non-radiation signal.
2. The method of claim 1, wherein the return portion of the interrogation beam is indicative of the generated non-radiation signal and a portion of the detected light other than the return portion of the interrogation beam and the excitation beam is indicative of the generated radiation signal.
3. The method of claim 1, further comprising: local optical scattering from the sample is detected.
4. The method of claim 1, wherein detecting the light comprises: the generated radiation signal and non-radiation signal are detected over time, and the method further comprises: the evolution time of the detected generated radiation signal and non-radiation signal is determined.
5. The method of claim 4, wherein generating the radiated and non-radiated signals in the sample occurs at a plurality of regions in the sample, and the method further comprises: a region belonging to the nucleus is identified in the plurality of regions based on the determined evolution time.
6. The method of claim 4, further comprising: determining at least one of the following based on the determined evolution time:
the thermal diffusivity of the sample is such that,
the electrical conductivity of the sample is such that,
the speed of sound in the sample,
the temperature of the sample is such that,
the density of the sample is such that,
the thermal capacity of the sample is such that,
the acoustic impedance of the sample is such that,
the tissue type of the sample, or
Molecular information of the sample.
7. The method of claim 4, further comprising:
an average pre-excitation signal is determined and,
the averaged post-excitation signal is determined based on a predetermined portion of the detected signal over time,
an amplitude is determined based on a difference between the determined average pre-excitation signal and the determined average post-excitation signal.
8. The method of claim 1, further comprising: based on the detected generated radiated PARS signal and non-radiated PARS signal, a function is used to determine a value.
9. The method of claim 8, wherein the value is a ratio of the detected generated radiated PARS signal to the non-radiated PARS signal.
10. The method of claim 1, further comprising: redirecting a portion of the returned interrogation beam and detecting interaction with the sample.
11. The method of claim 1, wherein the wavelength of the excitation beam is configured such that the sample absorbs two or more photons simultaneously, wherein a sum of energies of the two or more photons is equal to a predetermined energy or absorption.
12. The method of claim 1, wherein the wavelength of the excitation light beam is configured such that the sample absorbs two or more photons simultaneously, wherein the wavelength is equal to twice the predetermined wavelength.
13. The method of claim 12, wherein the predetermined wavelength is a wavelength in the ultraviolet, UV, range.
14. The method of claim 12, wherein the predetermined wavelength is a wavelength in the UVC range.
15. The method of claim 1, further comprising:
clustering the detected generated radiation signals and non-radiation signals based on shape using a clustering algorithm to determine a characteristic of the sample; and
Determining a cluster center based on the clustered signals and determining an image based on the clustered signals includes determining one or more colors based on the clustered signals.
16. The method of claim 1, wherein interrogating the sample with an interrogating beam comprises: the interrogation beam is moved over time throughout the sample to interrogate the sample over multiple regions.
17. The method of claim 16, further comprising: non-modulated scattering caused by spatially varying movement of the interrogating beam throughout the sample is estimated.
18. The method of claim 1, further comprising: a plurality of filtered instances of one of the generated signals is measured or stored, wherein the plurality of filtered instances includes an unfiltered instance of the signal and a filtered instance of the signal.
19. The method of claim 1, further comprising: determining a first image based on the detected generated radiation signal, determining a second image based on the detected generated non-radiation signal, comparing the first image to the second image, and determining one or more modifications to a final image of the sample based on the comparison.
20. The method of claim 1, wherein the interrogation beam comprises chirped pulses, and the method further comprises: the individual wavelength components of the interrogation beam are spatially separated.
21. The method of claim 1, wherein light from the sample is detected using a plurality of detectors.
22. The method of claim 1, wherein the excitation beam is focused in a smaller area than the detection beam is focused in.
23. The method of claim 1, for use in one or more of the following applications:
imaging of blood oxygen saturation;
imaging of tumor hypoxia;
imaging of wound healing, burn diagnosis or surgery;
imaging of the microcirculation;
imaging blood oxygenation parameters;
estimating blood flow in blood vessels flowing into and out of a tissue region;
imaging of a molecular specific target;
imaging angiogenesis in a preclinical tumor model;
clinical imaging of microcirculatory and macrocirculatory pigment cells;
imaging of the eye;
enhanced or replacement fluorescein angiography;
imaging a skin lesion;
imaging melanoma;
imaging basal cell carcinoma;
imaging hemangiomas;
Imaging psoriasis;
imaging eczema;
imaging dermatitis;
imaging the morse operation;
imaging to verify tumor margin resection;
imaging peripheral vascular disease;
imaging diabetic ulcers and/or pressure ulcers;
imaging burn;
performing plastic surgery;
microsurgery;
imaging of circulating tumor cells;
imaging melanoma cells;
imaging lymph node angiogenesis;
imaging a response to photodynamic therapy;
imaging a response to photodynamic therapy with a vascular ablation mechanism;
imaging the response to chemotherapy;
imaging the response to the anti-angiogenic drug;
imaging the response to radiation therapy;
estimating oxygen saturation using multi-wavelength photoacoustic excitation;
estimating venous oxygen saturation in the event that a pulse oximeter cannot be used;
estimating cerebral venous oxygen saturation and/or central venous oxygen saturation;
estimating oxygen flow and/or oxygen consumption;
imaging vascular beds and depth of invasion in barrett's esophageal cancer and/or colorectal cancer;
functional imaging during brain surgery;
assessment of internal bleeding and/or cauterization verification;
Imaging perfusion sufficiency of the organ and/or organ transplant;
imaging angiogenesis around islet transplantation;
imaging of skin grafts;
imaging the tissue scaffold and/or biological material to assess angiogenesis and/or immune rejection;
assisting in imaging of microsurgery;
avoid the guidance of cutting vessels and/or nerves;
imaging of contrast agents in clinical or preclinical applications;
identification of sentinel nodes;
non-invasive or minimally invasive identification of tumors in lymph nodes;
nondestructive testing of materials;
imaging of a gene-encoded reporter gene, wherein the gene-encoded reporter gene comprises tyrosinase, chromoprotein, and/or fluorescent protein for preclinical or clinical molecular imaging applications;
imaging an actively or passively targeted optically absorbing nanoparticle for molecular imaging;
imaging of thrombus;
classifying the existence time of thrombus;
replacement catheterization;
gastroenterology application;
single excitation pulse imaging over the entire field of view;
imaging tissue;
cell imaging;
imaging of scattered light from the surface of the object;
imaging of changes caused by absorption of scattered light; or (b)
Light absorbing non-contact imaging.
24. A method of visualizing features in a sample, the method comprising the steps of:
receiving a signal over a period of time, the signal comprising a non-radiated signal and a radiated signal from the sample;
determining a characteristic of the sample based on an evolution of the received signal over the period of time; and
an image is determined based on the determined features.
25. A method of visualizing features in a sample, the method comprising the steps of:
receiving signals over a period of time, the received signals being indicative of two or more unique absorption-based measurements in the sample;
determining a characteristic of the sample based on an evolution of the received signal over the period of time; and
an image is determined based on the determined features.
26. A method of visualizing features in a sample, the method comprising the steps of:
receiving a signal comprising a non-radiated signal and a radiated signal from the sample;
clustering the received one or more signals using a clustering algorithm to determine a characteristic of the sample; and
an image is determined based on the clustered signals.
27. A light absorbing remote sensing system for imaging a feature in a sample, comprising:
An excitation light source configured to generate a signal in the sample at an excitation location, the excitation light source focused below a surface of the sample;
an interrogation light source configured to interrogate the sample and toward an excitation location of the sample, the interrogation light source being focused beneath a surface of the sample, wherein a portion of at least one interrogation light source returned from the sample is indicative of at least some of the generated signals; and
a processor configured to analyze the generated signal as a function of time and to determine an image indicative of a feature in the sample.
28. A light absorbing remote sensing system for imaging a feature in a sample, comprising:
an excitation light source configured to generate a signal in the sample at an excitation location, the excitation light source focused below a surface of the sample;
an interrogation light source configured to interrogate the sample and toward an excitation location of the sample, the interrogation light source being focused beneath a surface of the sample; and
a detection source configured to detect light from the sample, wherein the detection source is configured to detect a portion of the interrogating light source returning from the sample, the returning portion of the interrogating light source being indicative of at least some of the generated signals; and
A processor configured to analyze the generated signal as a function of time and to determine an image indicative of a feature in the sample.
29. A computer-implemented method of visualizing features in a sample, the method comprising the steps of:
receiving one or more light absorbing remote sensing PARS signals;
clustering the received one or more PARS signals using a clustering algorithm to determine a characteristic of the sample; and
an image is determined based on the clustered PARS signals.
30. A light absorbing remote sensing PARS system for imaging a feature in a sample comprising:
an excitation light source configured to generate a signal in the sample at an excitation location, the excitation light source focused below a surface of the sample;
an interrogation light source configured to interrogate the sample and towards an excitation location of the sample, the interrogation light source being focused beneath a surface of the sample, a portion of at least one interrogation light source returned from the sample being indicative of the generated signal; and
a processor configured to execute a clustering algorithm to cluster the generated signals and to determine an image based on the clustered generated signals, the image being indicative of a feature in the sample.
31. A computer-implemented method of visualizing features in a sample, the method comprising the steps of:
receiving one or more signals;
clustering the received signals based on shape using a clustering algorithm to determine time domain features of the samples; and
an image is determined based on the clustered signals and the determined temporal features, the image including one or more colors used therein.
32. A computer-implemented method of visualizing features in a sample, the method comprising the steps of:
receiving a signal comprising a non-radiated signal and a radiated signal from the sample;
clustering the received one or more signals using a clustering algorithm to determine a characteristic of the sample; and
an image is determined based on the clustered signals.
33. A method of visualizing features in a sample, the method comprising the steps of:
receiving one or more signals;
clustering the received signals using a clustering algorithm based on a shape to determine features of the samples, the shape based on a vector; and
an image is determined based on the clustered signals and the determined features, the image including one or more colors used therein.
Applications Claiming Priority (7)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US63/187,789 | 2021-05-12 | ||
| IBPCT/IB2021/055380 | 2021-06-17 | ||
| US17/394,919 | 2021-08-05 | ||
| US63/241,170 | 2021-09-07 | ||
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| PCT/IB2022/054433 WO2022238956A1 (en) | 2021-05-12 | 2022-05-12 | Photoabsorption remote sensing (pars) imaging methods |
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