CN111493832A - Enface-OCT-based endoscopic imaging method - Google Patents
Enface-OCT-based endoscopic imaging method Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
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- A61B5/0066—Optical coherence imaging
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
- A61B5/0073—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence by tomography, i.e. reconstruction of 3D images from 2D projections
Abstract
An Enface-OCT-based endoscopic imaging method comprises the steps of obtaining, storing, calibrating, resampling and calculating a power spectrum of human body blood vessel data, obtaining continuous image data, calculating blood flow characteristic information, and obtaining a blood flow distribution characteristic and an Enface image by an Enface algorithm; en face imaging of a micro structure or a micro blood vessel can be realized, and the distribution and the change of the micro blood vessel of different layers of tissues near a human body lumen can be observed; is especially suitable for the definite diagnosis of diseases such as lung, digestive tract, coronary heart disease and the like and tumors, and provides reference for clinical medical research.
Description
The technical field is as follows:
the invention relates to the technical field of medical imaging, in particular to an Enface-OCT-based endoscopic imaging method.
(II) background technology:
optical Coherence Tomography (OCT) is an emerging biomedical Optical imaging technique that is based on low Coherence interference techniques and is capable of imaging strongly scattering media, including biological tissues, in depth. OCT is known as "optical biopsy" in the biological and medical fields due to its advantages of high resolution, no radiation, non-contact measurement, etc. The OCT utilizes backward scattering light of biological tissues of a sample arm to interfere with light of a reference arm, reconstructs a fault structure of the biological tissues through signal acquisition and data processing, forms three-dimensional imaging comprising depth information, and carries out in-vivo non-invasive detection and observation on internal structures of organisms.
The blood vessel imaging technology based on the Enface-OCT can realize high-precision imaging of the tiny blood vessels. Due to a series of outstanding advantages of being non-invasive, non-destructive, non-contact and independent of contrast agents, the Enface-OCT has become the most important research hotspot in the field of ophthalmic imaging.
The Enface-OCT is a transverse tomographic image technology which is formed by software operation processing on the basis of the traditional high-density B-scan, and can provide plane images of different depth levels of retina and choroid. Can analyze the changes of imaging of optic neuritis, optic disc fovea and glaucoma, and the pathological changes of optic disc neovascularization, and is a powerful tool for auxiliary diagnosis and evaluation of optic neuropathy.
However, the above studies are only for the imaging of blood flow contrast in vitro, especially in ophthalmology, and there are differences in structural and optical properties between the eyeball tissue and the human tissue, such as transparency, light wave penetration depth, tissue refractive index, etc. In the aspects of diagnosis and treatment of cardiovascular diseases, injury of digestive tract, esophagus or lung and tumor research, the blood flow radiography technology has very important significance, but because a method capable of imaging blood flow perfusion is lacked, the method for imaging the Enface-OCT cannot be directly used for blood flow imaging of other tissues of a human body, so that the clinical evaluation and diagnosis and treatment of related diseases are limited.
(III) the invention content:
the invention aims to provide an Enface-OCT-based endoscopic imaging method, which can make up for the defects of the prior art, is convenient to operate, can image the structure of tissues in a human body and the distribution of microvessels at different layers by utilizing the Enface-OCT imaging technology, and is favorable for diagnosing and evaluating blood flow related diseases.
The technical scheme of the invention is as follows: an Enface-OCT-based endoscopic imaging method is characterized by comprising the following steps:
(1) acquiring and storing human body blood vessel data through an OCT system as memory data;
(2) resampling the memory data obtained in the step (1) through a calibration algorithm to obtain resampled data;
(3) calculating the resampled data through a power spectrum to obtain original power data; performing calculation processing for generating continuous frames on the power original data to obtain continuous image data;
(4) and (4) after the continuous image data in the step (3) are obtained, calculating blood flow characteristic information in the continuous image data, processing the data by utilizing an Enface algorithm, solving the distribution characteristics of the blood flow, and further obtaining an Enface image of the blood flow.
The resampling in the step (2) is to resample data acquired based on a nonlinear wave number clock; the OCT system is characterized in that clock sampling signals of the OCT system are signals at equal time intervals, and a sweep light source is envelope signals with constantly-changed frequency and wavelength, so that the clock signals need to be converted into clock signals with integral-wavelength acquisition, and then the directly-acquired signals are resampled by using the converted signals.
And (3) performing power spectrum calculation in the step (3) by performing Fourier transform on the data after resampling and then performing logarithm operation to obtain power original data.
The continuous image data obtained in step (3) refers to 5 frames or more of image data acquired at each cross-sectional position of the blood vessel.
The processing of the data by the En face algorithm in the step (4) refers to obtaining B-frame data of blood flow characteristics or human tissue structure characteristic information from the continuous image data through calculation, splicing the B-frame data into rectangular volume data, obtaining projection distribution information of the blood flow or the human tissue structure, and finally obtaining an En face image.
The process of obtaining the En face image by obtaining the projection distribution information comprises the following steps:
① suppose Q is obtained1,Q2,Q3… … Qn are n consecutive image data, and each image is composed of m A-line data, wherein each A-line data can be represented by s pixel values,is recorded as:
② will be
The pixel values in (1) are rearranged in descending order of magnitude to obtain a new set of data
③ n successive image data are projected separately to obtain n data, each of which can be recorded as a new A-line data Vn(ii) a Respectively projecting m A-line data of each image to obtain one point data, namely V, in the new A-line datan＝[Vn1,Vn2,Vn3,......Vnm]；
④ n new A-line data constitute En face image data, i.e. Vn1,Vn2,Vn3,......VnmThus, an En face image can be obtained.
And m, n and s are positive integers greater than zero.
The projection in step ③ can be obtained by any one of the following formulas:
the method comprises the following steps: projecting by adopting the maximum value; namely, the maximum pixel value of each A-line data in the continuous image data is taken as the nth A-line data V in the En face imagenOf the m-th point data, i.e. the value of
The second method comprises the following steps: projecting by using the average of a segment of the maximum value, i.e. the average of the sum of the pixel values of the segment of each A-line data in successive image data descending from the maximum pixel value to the kth maximum value, i.e.
Wherein k is a positive integer, k is greater than or equal to 1 and less than or equal to s;
the third method comprises the following steps: using a sum of maxima, i.e. projecting the sum of pixel values of each A-line data in successive image data in descending order from the maximum pixel value to the kth maximum value, i.e.
Wherein k is a positive integer, k is greater than or equal to 1 and less than or equal to s;
the method four comprises the following steps: projecting by adopting the minimum value; namely, the minimum pixel value of each A-line data in the continuous image data is taken as the nth A-line data V in the En face imagenOf the m-th point data, i.e. the value of
The blood flow characteristic information in the continuous image data in the step (4) is acquired, and the method comprises the following steps:
① extracting the amplitude component A and the phase component omega of the continuous image data in the step (3), wherein each frame of image data can obtain an amplitude component and a phase component;
②, obtaining the amplitude component difference delta A and the phase component difference delta omega between each frame of image at the same blood vessel position;
③ the blood flow information is obtained by a method Δ a based on the amplitude component difference or by a method Δ a + Δ ω based on both the amplitude and phase differences.
The basic principle of the blood flow radiography algorithm processing in the invention is as follows: other tissue parts in the human body are static, and blood flows in blood vessels, so that moving blood flow information can be obtained by means of subtraction according to the difference between adjacent frames. The specific method for processing the blood flow angiography algorithm comprises the following steps: the OCT system acquisition device acquires 5 frames or more than 5 frames of image data at the same position, and each frame of image data comprises an amplitude component A and a phase component omega. The blood flow information may be obtained by only the method Δ a based on the amplitude component difference, or may be obtained by two types of differences Δ a + Δ ω based on the amplitude and phase. The method for calculating the blood flow distribution information only by using the amplitude difference does not require the stability of the phase, has relatively low requirement on an acquisition device, and is relatively simple in processing complexity; the method for obtaining blood flow distribution information based on amplitude and phase difference can obtain better effect on imaging details and vessel connectivity, but the processing time is relatively long. Which method is used can be selected according to actual needs.
The invention has the advantages that: the En face imaging technology is applied to endoscopic imaging of tissues in a human body, and En face imaging can be performed on the fine structure or the microvasculature of the tissues in the human body, so that the distribution and the change of the microvasculature of different layers of the tissues near an inner cavity channel of the human body are clearly observed, a clinical basis is provided for pathogenesis and definite diagnosis of some blood flow related diseases, and the limitation on clinical evaluation and diagnosis and treatment of the related diseases caused by the lack of a method capable of imaging blood flow perfusion is broken through; is especially suitable for the pathogenesis and definite diagnosis of diseases such as lung or digestive tract injury, coronary heart disease and the like and tumors, and can provide reference for clinical medical research.
(IV) description of the drawings:
FIG. 1 is a schematic view of a flow structure of an endoscopic imaging method based on Enface-OCT in accordance with the present invention.
Fig. 2 is a schematic flow chart of main steps of an En face algorithm processing in an endoscopic imaging method based on En face-OCT according to the present invention.
(V) specific embodiment:
example (b): an Enface-OCT-based endoscopic imaging method is shown in figures 1 and 2 and is characterized by comprising the following steps:
(1) acquiring and storing human body blood vessel data through an OCT system as memory data;
(2) resampling the memory data obtained in the step (1) through a calibration algorithm to obtain resampled data;
(3) calculating the resampled data through a power spectrum to obtain original power data; performing calculation processing for generating continuous frames on the power original data to obtain continuous image data;
(4) and (4) after the continuous image data in the step (3) are obtained, calculating blood flow characteristic information in the continuous image data, processing the data by utilizing an Enface algorithm, solving the distribution characteristics of the blood flow, and further obtaining an Enface image of the blood flow.
The resampling in the step (2) is to resample data acquired based on a nonlinear wave number clock; the OCT system is characterized in that clock sampling signals of the OCT system are signals at equal time intervals, and a sweep light source is envelope signals with constantly-changed frequency and wavelength, so that the clock signals need to be converted into clock signals with integral-wavelength acquisition, and then the directly-acquired signals are resampled by using the converted signals.
And (3) performing power spectrum calculation in the step (3) by performing Fourier transform on the data after resampling and then performing logarithm operation to obtain power original data.
The continuous image data obtained in step (3) refers to 5 frames or more of image data acquired at each cross-sectional position of the blood vessel.
The processing of the data by the En face algorithm in the step (4) refers to obtaining B-frame data of blood flow characteristics or human tissue structure characteristic information from the continuous image data through calculation, splicing the B-frame data into rectangular volume data, obtaining projection distribution information of the blood flow or the human tissue structure, and finally obtaining an En face image.
The process of obtaining the En face image by obtaining the projection distribution information comprises the following steps:
① suppose Q is obtained1,Q2,Q3… … Qn are n consecutive image data, and each image is composed of m A-line data, wherein each A-line data can be represented by s pixel values, which are expressed as:
② will be
The pixel values in (1) are rearranged in descending order of magnitude to obtain a new set of data
③ n successive image data are projected separately to obtain n data, each of which can be recorded as a new A-line data Vn(ii) a Respectively projecting m A-line data of each image to obtain one point data, namely V, in the new A-line datan＝[Vn1,Vn2,Vn3,......Vnm]；
④ n new A-line data constitute En face image data, i.e. Vn1,Vn2,Vn3,......VnmThus, an En face image can be obtained.
And m, n and s are positive integers greater than zero.
The projection in step ③ can be obtained by any one of the following formulas:
the method comprises the following steps: projecting by adopting the maximum value; namely, the maximum pixel value of each A-line data in the continuous image data is taken as the nth A-line data V in the En face imagenOf the m-th point data, i.e. the value of
The second method comprises the following steps: projecting by using the average of a segment of the maximum value, i.e. the average of the sum of the pixel values of the segment of each A-line data in successive image data descending from the maximum pixel value to the kth maximum value, i.e.
Wherein k is a positive integer, k is greater than or equal to 1 and less than or equal to s;
the third method comprises the following steps: using a sum projection of a maximum, i.e. from the maximum image with each a-line data in successive image dataThe pixel values being projected in descending order to the sum of the pixel values of the section of the k-th maximum, i.e.
Wherein k is a positive integer, k is greater than or equal to 1 and less than or equal to s;
the method four comprises the following steps: projecting by adopting the minimum value; namely, the minimum pixel value of each A-line data in the continuous image data is taken as the nth A-line data V in the En face imagenOf the m-th point data, i.e. the value of
The blood flow characteristic information in the continuous image data in the step (4) is acquired, and the method comprises the following steps:
① extracting the amplitude component A and the phase component omega of the continuous image data in the step (3), wherein each frame of image data can obtain an amplitude component and a phase component;
②, obtaining the amplitude component difference delta A and the phase component difference delta omega between each frame of image at the same blood vessel position;
③ the blood flow information is obtained by a method Δ a based on the amplitude component difference or by a method Δ a + Δ ω based on both the amplitude and phase differences.
As shown in fig. 1, data of a blood vessel of a human body is first acquired and stored as memory data by an OCT system. Generally, a clock sampling signal of an OCT system is a signal at equal time intervals, and a swept-frequency light source is an envelope signal whose frequency and wavelength are constantly changed, so that the clock signal needs to be converted into a clock signal capable of collecting an integer wavelength, and then the directly collected signal is resampled by using the converted signal. And obtaining sampled data, and obtaining power original data through power spectrum calculation. The power raw data is subjected to a continuous frame calculation algorithm to obtain continuous image data, and the data should be 5 frames or more of image data at each position. And performing En face algorithm processing on the continuous image data, namely splicing the continuous image data into rectangular volume data, and obtaining a structural distribution characteristic on a certain interested projection surface by using the En face algorithm to obtain a structural En face image.
After acquiring continuous image data, processing of a blood flow angiography algorithm can be performed, blood flow characteristics are calculated on the continuous image data, then processing of an En face algorithm is performed, all data of blood flow characteristic information B-frame obtained through calculation are spliced into rectangular volume data, characteristics of blood flow distribution on a certain interested projection surface are obtained through the En face algorithm, and a blood flow En face image is obtained. The specific method for processing the blood flow angiography algorithm comprises the following steps: the OCT system acquisition device acquires 5 frames or more than 5 frames of image data at the same position, and each frame of image data comprises an amplitude component A and a phase component omega. The blood flow information may be obtained by only the method Δ a based on the amplitude component difference, or may be obtained by two types of differences Δ a + Δ ω based on the amplitude and phase. The method for calculating the blood flow distribution information only by using the amplitude difference does not require the stability of the phase, has relatively low requirement on an acquisition device, and is relatively simple in processing complexity; the method for obtaining blood flow distribution information based on amplitude and phase difference can obtain better effect on imaging details and vessel connectivity, but the processing time is relatively long. Which method is used can be selected according to actual needs.
As shown in fig. 2, the process of the En face image comprises the following steps:
① suppose Q is obtained1,Q2,Q3… … Qn are n consecutive image data, and each image is composed of m A-line data, wherein each A-line data can be represented by s pixel values, which are expressed as:
② will be
The pixel values in (1) are rearranged in descending order of magnitude to obtain a new set of data
③ n successive image data are projected separately to obtain n data, each of which can be recorded as a new A-line data Vn(ii) a Respectively projecting m A-line data of each image to obtain one point data, namely V, in the new A-line datan＝[Vn1,Vn2,Vn3,......Vnm]；
④ n new A-line data constitute En face image data, i.e. Vn1,Vn2,Vn3,......VnmThus, an En face image can be obtained.
And m, n and s are positive integers greater than zero.
The projection method in step ③ is specifically expressed by the following formulas, and any of them may be specifically adopted:
the method comprises the following steps: projecting by adopting the maximum value; namely, the maximum pixel value of each A-line data in the continuous image data is taken as the nth A-line data V in the En face imagenOf the m-th point data, i.e. the value of
The second method comprises the following steps: projecting by using the average of a segment of the maximum value, i.e. the average of the sum of the pixel values of the segment of each A-line data in successive image data descending from the maximum pixel value to the kth maximum value, i.e.
Wherein k is a positive integer, 1. ltoreq. k. ltoreq.s.
The third method comprises the following steps: using a sum of maxima, i.e. projecting the sum of pixel values of each A-line data in successive image data in descending order from the maximum pixel value to the kth maximum value, i.e.
Wherein k is a positive integer, 1. ltoreq. k. ltoreq.s.
The method four comprises the following steps: with minimum valueProjecting; namely, the minimum pixel value of each A-line data in the continuous image data is taken as the nth A-line data V in the En face imagenOf the m-th point data, i.e. the value of
Claims (9)
1. An Enface-OCT-based endoscopic imaging method is characterized by comprising the following steps:
(1) acquiring and storing human body blood vessel data through an OCT system as memory data;
(2) resampling the memory data obtained in the step (1) through a calibration algorithm to obtain resampled data;
(3) calculating the resampled data through a power spectrum to obtain original power data; performing calculation processing for generating continuous frames on the power original data to obtain continuous image data;
(4) and (4) after the continuous image data in the step (3) are obtained, calculating blood flow characteristic information in the continuous image data, processing the data by utilizing an Enface algorithm, solving the distribution characteristics of the blood flow, and further obtaining an Enface image of the blood flow.
2. The Enface-OCT-based endoscopic imaging method according to claim 1, wherein the resampling in step (2) is based on resampling of data acquired on a non-linear wavenumber clock; the OCT system is characterized in that clock sampling signals of the OCT system are signals at equal time intervals, and a sweep light source is envelope signals with constantly-changed frequency and wavelength, so that the clock signals need to be converted into clock signals with integral-wavelength acquisition, and then the directly-acquired signals are resampled by using the converted signals.
3. The Enface-OCT-based endoscopic imaging method according to claim 1, wherein the power spectrum calculation in step (3) is a logarithm operation after Fourier transform of the resampled data, resulting in raw power data.
4. The Enface-OCT-based endoscopic imaging method according to claim 3, wherein the continuous image data obtained in step (3) refers to 5 frames or more of image data acquired at each cross-sectional location of the blood vessel.
5. The Enface-OCT-based endoscopic imaging method according to claim 1, wherein the processing of data by the Enface algorithm in step (4) means that B-frame data of blood flow characteristics or human tissue structure characteristics information is calculated from continuous image data, and is spliced into rectangular volume data, and projection distribution information of blood flow or human tissue structure is obtained, and finally an Enface image is obtained.
6. The Enface-OCT-based endoscopic imaging method according to claim 5, wherein the process of obtaining the Enface image by acquiring the projection distribution information in step (4) comprises the following steps:
① suppose Q is obtained1,Q2,Q3… … Qn are n consecutive image data, and each image is composed of m A-line data, wherein each A-line data can be represented by s pixel values, which are expressed as:
② will be
The pixel values in (1) are rearranged in descending order of magnitude to obtain a new set of data
③ n successive image data are projected separately to obtain n data, each of which can be recorded as a new A-line data Vn(ii) a Respectively projecting m A-line data of each image to obtain one point data, namely V, in the new A-line datan＝[Vn1,Vn2,Vn3,......Vnm]；
④ n new A-line data constitute En face image data, i.e. Vn1,Vn2,Vn3,......VnmThus, an En face image can be obtained.
7. The Enface-OCT-based endoscopic imaging method of claim 6, wherein m, n, s are all positive integers greater than zero.
8. An Enface-OCT based endoscopic imaging method according to claim 6, wherein the projection in step ③ is obtainable by an equation selected from the group consisting of:
the method comprises the following steps: projecting by adopting the maximum value; namely, the maximum pixel value of each A-line data in the continuous image data is taken as the nth A-line data V in the En face imagenOf the m-th point data, i.e. the value of
The second method comprises the following steps: projecting by using the average of a segment of the maximum value, i.e. the average of the sum of the pixel values of the segment of each A-line data in successive image data descending from the maximum pixel value to the kth maximum value, i.e.
Wherein k is a positive integer, k is greater than or equal to 1 and less than or equal to s;
the third method comprises the following steps: projection using a sum of maxima, i.e. each A-lin in successive image datae projection of the sum of the pixel values of the section from the maximum pixel value to the k-th maximum value in descending order, i.e.
Wherein k is a positive integer, k is greater than or equal to 1 and less than or equal to s;
the method four comprises the following steps: projecting by adopting the minimum value; namely, the minimum pixel value of each A-line data in the continuous image data is taken as the nth A-line data V in the En face imagenOf the m-th point data, i.e. the value of
9. The Enface-OCT-based endoscopic imaging method according to claim 5, wherein the blood flow characteristic information acquisition in the continuous image data in step (4) comprises the following steps:
① extracting the amplitude component A and the phase component omega of the continuous image data in the step (3), wherein each frame of image data can obtain an amplitude component and a phase component;
②, obtaining the amplitude component difference delta A and the phase component difference delta omega between each frame of image at the same blood vessel position;
③ the blood flow information is obtained by a method Δ a based on the amplitude component difference or by a method Δ a + Δ ω based on both the amplitude and phase differences.
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