CN113253330A - Gamma ray radiation imaging device and energy calibration method - Google Patents

Abstract

The calibration of the energy is performed with high accuracy. A gamma-ray radiation imaging apparatus according to an embodiment includes an acquisition unit and a calibration unit. The acquisition unit acquires calibration data of radiation incident on a detector, the calibration data including a first spectrum collected when radiation from a radioisotope in a scintillator crystal is irradiated on the detector. The calibration unit applies the energy signal measured by the detector to nonlinear energy correction, and performs energy calibration by adjusting parameters of the nonlinear energy correction so as to optimize matching between a reference value of a reference spectrum indicating absorbed radiant energy and calibrated energy generated when the first energy spectrum of the calibration data is applied to the nonlinear energy correction.

Description

Gamma ray radiation imaging device and energy calibration method

Reference to related applications

The present application enjoys the benefit of priority from U.S. patent application 16/788,741, filed on 12/2020 and Japanese patent application 2020-159887, filed on 24/9/2020, which are incorporated herein by reference in their entirety.

Technical Field

Embodiments disclosed herein relate generally to a gamma-ray radiation imaging apparatus and an energy calibration method.

Background

In gamma-ray radiation imaging such as PET (Positron Emission Tomography) imaging and SPECT (Single Photon Emission Point Tomography) imaging, the response of a detector, which is a function of the energy of an incident gamma ray, may deviate from a linear response and have a nonlinear response.

An example of the nonlinear response is a case where a silicon photomultiplier (SiPM) is used as a detector. Another example of the nonlinear response is a case where the energy of the incident gamma ray is estimated using a threshold exceeding time (ToT).

Therefore, in this case, it is preferable to perform the calibration of the energy with high accuracy.

Documents of the prior art

Patent document

Patent document 1: specification of U.S. Pat. No. 6150655

Disclosure of Invention

One of the technical problems to be solved by the embodiments disclosed in the present specification and the like is to perform energy calibration with high accuracy. However, the technical problems to be solved by the embodiments disclosed in the present specification and the like are not limited to the above technical problems. Technical problems corresponding to respective effects of the respective configurations described in the embodiments described below can be also addressed as other technical problems to be solved by the embodiments disclosed in the present specification and the like.

A gamma-ray radiation imaging apparatus according to an embodiment includes an acquisition unit and a calibration unit. The acquisition unit acquires calibration data of radiation incident on a detector, the calibration data including a first spectrum collected when radiation from a radioisotope in a scintillator crystal is irradiated on the detector. The calibration unit applies the energy signal measured by the detector to nonlinear energy correction, and performs energy calibration by adjusting parameters of the nonlinear energy correction so as to optimize matching between a reference value of a reference spectrum indicating absorbed radiant energy and calibrated energy generated when the first energy spectrum of the calibration data is applied to the nonlinear energy correction.

Drawings

Fig. 1A is a diagram showing a silicon photomultiplier (SiPM) detector including 2 optical elements incident on each microcell according to one embodiment.

Fig. 1B is a diagram showing an SiPM detector including 6 optical elements that enter each microcell according to one embodiment.

Fig. 1C is a graph showing the nonlinear energy response of an SiPM detector according to an embodiment.

FIG. 2A is a graph illustrating a Time-Over-Threshold (TOT) measurement curve according to one embodiment.

FIG. 2B is a graph illustrating a graph of TOT as a function of the ratio of peak height to threshold, according to one embodiment.

Fig. 3 is a diagram showing how the total of the raw measurement energy of the 1-channel detection event and the raw measurement energy of the 2-channel detection event differs according to one embodiment.

FIG. 4 is a graph showing the frequency spectrum of the Lutetium Isotope 176 (Lutitium Isotope 176: Lu-176) plotted as a function of measured energy signal, according to one embodiment.

FIG. 5 is a flow diagram of a method 100 of performing energy calibration and reconstructing a Positron Emission Tomography (PET) image, under an embodiment.

Fig. 6 is a diagram showing a curve of an energy calibration model curve-fitted to energy calibration data according to an embodiment.

FIG. 7 is a Lu-176 energy level diagram of a physics-based model of the Lu-176 spectrum of an embodiment.

FIG. 8 is a graph illustrating spectra associated with various attenuation paths/emission processes that contribute to the Lu-176 spectrum, according to one embodiment.

Figure 9A is a perspective view of a PET scanner of one embodiment.

Fig. 9B is a diagrammatic view of a PET scanner of an embodiment.

Fig. 10 is a schematic diagram of a scattering process in the imaging scanner in the presence of a radiation source that emits radiation of a single energy according to one embodiment.

Fig. 11A is a graph showing plots of absorbed radiation contributed by the various scattering processes shown in fig. 10 when the detector has the correct energy resolution.

FIG. 11B is a graph showing plots of absorbed radiation contributed by the various scattering processes shown in FIG. 10 when the detector has limited energy resolution.

Detailed Description

Embodiments of a gamma-ray radiation imaging apparatus and an energy calibration method will be described in detail below with reference to the drawings.

First, the configuration of the gamma ray radiation imaging apparatus according to the embodiment will be briefly described with reference to fig. 9A and 9B.

Fig. 9A and 9B show a non-limiting example of a PET scanner 200 configured with detector modules (i.e., Gamma-Ray Detectors: GRDs) arranged in a ring shape. Multiple arrays of detector elements can be included in each detector module. GRDs include an array of scintillator crystals that convert gamma rays into scintillation photons (wavelengths of light, infrared, ultraviolet, etc.) that are detected by photodetectors. In the non-limiting example shown in fig. 9A and 9B, the light detector is a photomultiplier tube (PMT) that is much larger than the individual scintillator crystal elements. In a preferred embodiment, the photodetectors are silicon photomultipliers (sipms) capable of having a detection cross-section that approximates the cross-sectional area of each scintillator crystal element, with a one-to-one correspondence between the crystals and the photodetectors. If the photodetector is larger than the crystal so that a single photodetector is used to detect the optical signals from multiple crystals, the position can be determined using Anger arithmetic (Anger arithmetric). However, when there is a one-to-one correspondence between the crystal and the photodetector, the shelf operation is not necessarily required.

Fig. 9A and 9B show a non-limiting example of a PET scanner 200 that can implement the methods 100 and 160. The PET scanner 200 includes a plurality of Gamma Ray Detectors (GRDs) (e.g., GRDs 1, GRD2 based on GRDN) each configured as a rectangular detector module. According to one embodiment, the detector ring comprises 40 GRDs. In another embodiment, there are 48 GRDs, with more GRDs being used in order to increase the inner diameter size of the PET scanner 200.

Each GRD can include a two-dimensional arrangement of individual detector crystals that absorb gamma rays and emit scintillation photons. Scintillation photons can be detected by a two-dimensional array of photomultiplier tubes (PMTs) also disposed in the GRDs. A light guide can be arranged between the array of detector crystals and the PMT.

Alternatively, the scintillation photons can be detected by an array of silicon photomultiplier tubes (sipms), each detector crystal can have a SiPM.

Each light detector (e.g., PMT or SiPM) is capable of generating an analog signal that represents the time at which the scintillation event occurred and the energy of the gamma ray at which the detection event occurred. Further, photons emitted from 1 detector crystal can be detected by a plurality of photodetectors, and a detector crystal corresponding to a detection event can be determined based on analog signals generated by the photodetectors using, for example, a tray logic and crystal decoding.

Fig. 9B shows a schematic diagram of a PET scanner system including a gamma ray (γ ray) photon counting detector (GRD) configured to detect gamma rays emitted from the object OBJ. The GRD can measure timing, position, and energy corresponding to each gamma ray detection. In one embodiment, as shown in fig. 9A and 9B, the gamma-ray detectors are arranged in a ring. The detector crystal may be a scintillator crystal having individual scintillator elements arranged in a two-dimensional array, which may be any known scintillation material. The PMT may be configured to detect light from each scintillator element by a plurality of PMTs, and to perform a angusta operation and a crystal decoding of a scintillation event.

Fig. 9B shows an example of the arrangement of the PET scanner 200, in which the object OBJ is placed on the bed 216, and the GRD modules GRD1 to GRDN are arranged in the circumferential direction around the object OBJ and the bed 216. The GRD is fixedly coupled to a circular assembly 220 fixedly coupled to a frame 240. The gantry 240 houses most of the PET imaging apparatus. The gantry 240 of the PET imaging apparatus further includes an opening through which the subject OBJ and the bed 216 can pass, and gamma rays radiated in the opposite direction from the subject OBJ by annihilation events can be detected by the GRD, and simultaneous occurrence of gamma ray pairs can be determined using timing and energy information.

Also shown in FIG. 9B are the circuitry and hardware for collecting, saving, processing, and distributing the gamma ray detection data. The circuitry and hardware includes processing circuitry 270, a network controller 274, memory 278, and a data collection system (DAS) 276. The PET imaging device also includes a data channel that routes the detection measurements from the GRD to the DAS276, processing circuitry 270, memory 278, and network controller 274. DAS276 is capable of controlling the acquisition, digitization, and routing of detection data from the detectors. In one embodiment, DAS276 controls the action of bed 216. As described in the present specification, the processing circuit 270 executes functions including reconstruction of an image based on the detection data, pre-reconstruction processing of the detection data, and post-reconstruction processing of the image data.

The processing circuit 270 is a processor that executes programs to realize functions corresponding to the programs. For example, processing functions (not shown) such as an acquisition function, a calibration function, a detection function, and a generation function, which will be described later, are stored in the memory 278 in the form of programs that can be executed by a computer. In this case, the processing circuit 270 reads out and executes the programs from the memory 278, thereby realizing the functions corresponding to the respective programs. Alternatively, instead of storing the program in the memory 278, the functions may be directly incorporated into the circuit of the processor as logic circuits. In other words, the processing circuit 270 has processing functions such as an acquisition function, a calibration function, a detection function, and a generation function. The acquisition function, the calibration function, the detection function, and the generation function are examples of the acquisition unit, the calibration unit, the detection unit, and the generation unit, respectively. For example, the acquisition function acquires calibration data of radiation incident on the detector, the calibration data including a first spectrum collected when radiation from a radioisotope in the scintillator crystal is irradiated to the detector. The calibration function applies the energy signal measured by the detector to nonlinear energy correction, and adjusts parameters of the nonlinear energy correction so as to optimize matching between a reference value of a reference spectrum indicating the absorbed radiation energy and the calibrated energy generated when the first spectrum of the calibration data is applied to the nonlinear energy correction, thereby performing energy calibration.

The processing circuit 270 can be configured to perform the various steps of the methods 100 and 160 described herein, as well as variations thereof. The processing Circuit 270 can include a CPU that can be implemented as individual Logic gates, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), or other Complex Programmable Logic Devices (CPLDs). The installation of the FPGA or CPLD may be coded in VHDL, Verilog, or other hardware description languages, which may be stored directly or as separate electronic memory in electronic memory within the FPGA or CPLD. Further, the memory may be nonvolatile such as a ROM, EPROM, EEPROM, or flash memory. The memory may also be volatile, such as static or dynamic RAM, and a processor, such as a microcontroller or microprocessor, is provided for managing the electronic memory, and for managing the interaction between the FPGA or CPLD and the memory.

Alternatively, the CPU within processing circuit 270 may be capable of executing a computer program comprising a set of computer readable commands to perform the various steps of methods 100 and 160, stored in the non-transitory electronic memory and/or hard drive, CD, DVD, flash drive or other known storage medium described above. The computer-readable command may be provided as a processor such as an Xeon processor of intel, usa or an Opteron processor of AMD, usa, an operating system such as microsoft VISTA, UNIX (registered trademark), Solaris, LINUX (registered trademark), Apple, MAC-OS, a utility program executed in combination with another operating system known to those skilled in the art, a daemon, or a component of the operating system, or a combination thereof. Further, the CPU can be installed as a plurality of processors that coordinate in parallel to execute commands.

Memory 278 may be a hard disk drive, CD-ROM drive, DVD drive, flash drive, RAM, ROM, or any other electronic storage device known in the art.

The network controller 274, such as an Intel Ethernet (registered trademark) PRO network interface card of Intel Corporation of the united states, can interface various parts of the PET imaging apparatus. In addition, the network controller 274 can also interface with an external network. For understanding, the external network may be a public network such as the internet, a private network such as a LAN or WAN network, or any combination thereof, and may include a PSTN or ISDN sub-network. The external network may be wired, such as an ethernet network, or wireless, such as a cellular network including EDGE, 3G, and 4G wireless cellular systems. The wireless network may also be WiFi, bluetooth (registered trademark), or other known wireless form of communication.

The embodiments described below relate to energy detection in a gamma-ray detector, and more particularly, to calibrating energy correction in a gamma-ray detector using a single (or several if any) energy source having a spectral signature greater than a full single energy peak.

The background of the embodiments will be briefly described. In PET imaging, a tracer is introduced into a patient, and the agent is accumulated in a specific location in the patient's body by its physical and biomolecular properties. The tracer emits a positron which, upon collision with an electron, brings about an annihilation event, generating 2 gamma rays (in 511keV) that advance approximately 180 degrees apart.

PET imaging systems use detectors arranged around the patient to detect simultaneous pairs of gamma rays. The ring of detectors can be used to detect gamma rays from various angles. Accordingly, the PET scanner may be substantially cylindrical in shape in order to maximize the capture of isotropic radiation. A PET scanner can be made up of thousands of crystals (e.g., Lutetium orthosilicate (LYSO) or other scintillation crystals) configured as a two-dimensional scintillator array, which are packaged in a module with photodetectors for measuring the light pulses from each scintillation event. For example, light from each element of the scintillator crystal array can be shared among a plurality of Photomultiplier tubes (PMT), or can be detected by Silicon Photomultiplier tubes (Silicon photom) that correspond one-to-one to the elements of the scintillator crystal array.

In order to reconstruct the spatio-temporal distribution of the tracer by means of the tomographic reconstruction principle, each detected event is characterized with respect to its energy (i.e. the amount of light generated), its position and its timing. By detecting the 2 gamma rays and extracting a Line between their positions, that is, a Line-Of-Response (LOR), it is possible to determine a position where the possibility Of the original decay (differentiation) exists. The timing information may be used to determine a statistical distribution Of annihilation along the LOR based on Time-Of-Flight (TOF) information Of the 2 gamma rays. By accumulating a large number of LORs, tomographic reconstruction can be performed to determine a volumetric image of the spatial distribution of the radioactivity within the patient (tracer density, etc.).

SPECT is similar to PET except that it uses a collimator to limit the solid angle of gamma rays incident on each detector element (e.g., each element of a scintillator crystal array), does not need to occur simultaneously in order to determine LOR, but can be reconstructed using a single gamma ray detection event.

In addition to position information (e.g., LOR) and timing information (e.g., TOF), the detectors of PET and SPECT systems are also capable of acquiring and using energy information in the image reconstruction process. Energy calibration is important for all PET detectors. For example, in order to substantially reduce the contribution of scatter to the final image, a proper energy calibration enables energy cutting.

In many PET detectors, the energy response of the detector is substantially linear. In these cases, the energy calibration can be performed using a single energy. In the case of a linear response, the energy calibration includes determining a scaling factor that transforms the measured signal level corresponding to a 511keV gamma ray to a desired target value.

However, the energy measurement may deviate from the ideal linear response due to non-linearity of the measurement process, for example, due to practical considerations associated with light/charge sharing between channels in multi-channel gamma ray detection (e.g., gamma ray energy is absorbed in multiple detectors/channels in a manner that can be generated due to compton scattering). Therefore, improved techniques are desired for calibrating energy measurements of pixelated gamma-ray detectors.

One embodiment of Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) imaging relates to the ability to determine the location, time, and energy of detected gamma rays. For example, scattered random gamma rays can be distinguished from concurrent gamma rays that occur from the same positron annihilation event using time and energy windows. Thus, the reliability of concurrent identification depends on the timing and energy calibration accuracy.

Accordingly, improved energy calibration of gamma-ray detectors is desired. However, in these improved methods, it is necessary to avoid an increase in time and cost of energy calibration as much as possible. For example, in the improved method described above, the number of energy calibration sources and the number of steps of the calibration process need to be reduced, if possible. That is, the improved calibration method should aim to provide a more accurate energy calibration while being more efficient in terms of time and cost.

The response of the detector as a function of the input energy may deviate from the ideal linear response due to various practical considerations. To correct for this nonlinear energy response, the methods and apparatus described herein use an improved energy calibration method to generate a substantially linear corrected energy value.

There are many reasons for non-linearity in the gamma ray detection process. For example, a photosensor in a gamma ray detector using a silicon photomultiplier tube (SiPM) as a scintillator substrate, and a time (ToT) method using a threshold value exceeding that used for amplitude estimation may introduce significant energy value nonlinearity. This correction for non-linearity is important in order to obtain correct energy information, especially in multi-channel detection events where energy gamma rays are dispersed across multiple crystals and detected (e.g., energy is likely to be shared among multiple crystals due to compton scattering). Additional details regarding the impact of energy non-linearity on the detection of multichannel events are provided below with reference to fig. 3.

The energy signal can be obtained by converting energy accumulated in the crystal into an electric signal, and then the electric signal can be digitized. The digitization process can be performed by various methods. Among the methods for digitizing the gamma ray energy measurement value, the time-of-flight (TOT) method that exceeds a threshold value has an advantage of being cost-effective and can be easily applied to applications requiring a high channel density. The TOT value is a monotonically increasing function of the absorbed energy of a particular channel, but the relationship of TOT to actual energy may not be perfectly linear. The response of the non-linear detector, as well as the response of other non-linear detectors, can be modified using the methods described herein.

In the case where the sensitive elements of the detector are crystal arrays, there is a possibility that sharing of incident gamma ray energy between multiple crystals/readout channels may occur due to inter-crystal scattering (compton scattering, etc.), light sharing, charge sharing. That is, the energy from a single 511keV gamma ray is shared/distributed among multiple channels, so each of these channels detects only a portion of the total energy of the gamma ray. However, the total energy of the original gamma rays can be recovered by summing the energies from the respective channels only when the nonlinearity is corrected.

In a multi-channel detection event, the energy detected at a given channel is in the range of 511keV to the lower detection limit of the gamma ray detector (e.g., 80keV), and thus the energy calibration of the gamma ray detector is preferably to that range.

The 1 method of calibration over a wide range of energies is to use multiple sources emitting different energies (e.g., different isotopes, etc.). For example, gamma rays of different energies can be provided using a radioactive background from an external gamma ray source or the crystal.

In contrast to multi-source calibration methods, the methods described herein use gamma-ray sources that themselves possess highly structured energy characteristics throughout a large number of different gamma-ray energies (e.g., a continuum of large numbers of discrete energies and/or energies). Thus, energy calibration of a wide range of gamma ray energies can be performed using a single gamma ray source (e.g., lutetium isotope 176, Lu-176) or several, in embodiments 2 gamma ray sources (e.g., the second source may be any of the germanium isotopes, Ge-68, or fluorine isotopes 18, F-18).

For example, gamma-ray detectors using silicon photomultipliers and dynamic time threshold amplitude estimation exhibit significant non-linearity of energy. In certain embodiments, the method described herein uses more than 2 spectral features of the Lu-176 background spectrum (background spectrum) to extract the nonlinear correction coefficients. Lutetium (Lu) based scintillators are often used for time of flight (TOF) measurements of Positron Emission Tomography (PET) detectors. Lu-176 has a weak radioactivity and therefore accumulates background spectra at any time during the time when the PET scanner is not in use. This background spectrum is assembled into a PET scanner as part of the scintillator used in detection, so no external radiation source is required. Therefore, additional labor for acquiring the energy calibration spectrum can be minimized.

Here, when referring to the drawings, the same reference numerals denote the same or corresponding elements throughout the several drawings, fig. 1A to 1C show a first cause of nonlinearity generated in a silicon photomultiplier (SiPM), and fig. 2A and 2B show a second cause of nonlinearity generated when a Time Over Threshold (TOT) is used as a measure of gamma ray energy.

Fig. 1A shows an SiPM detector for the low-flux case where 2 photons are incident on 2 microcells located within a two-dimensional (2D) array of 35 microcells (i.e., 5 microcells by 7 microcells). In many cases, there are thousands of microcells in the SiPM detector, but here, as a simplified example for explanation, a reduced number of microcells are used. Fig. 1B shows the SiPM detector with a moderate flux of 6 photons incident on 6 of the 35 microcells. SiPM is a photodetector formed as a two-dimensional arrangement of fine Geiger-Mode Avalanche Photodiode (G-APD) elements called microcells. This architecture overcomes the disadvantages of a single G-APD because the amplitude of the output pulse of the SiPM is proportional to the number of photons incident on the surface of the device (over a range of intensities). However, if the photon flux becomes sufficiently large and the probability of 2 photons entering the same microcell cannot be ignored, the signal as a function of the number of incident photons starts to invert (roll over), becoming nonlinear. This non-linearity is illustrated in fig. 1C, where the gamma ray energy, represented along the horizontal axis, is proportional to the number of optical photons incident on the SiPM. In a PET detector, the optical photon flux may become large (for example, several thousand photons are generated for each gamma ray of 511keV), and SiPM requires a large dynamic range.

As described above, the output signal of the SiPM is the sum of the signals of the light-receiving microcells, and therefore the output signal is correlated with the number of incident photons. The dynamic range is determined by the number of cells within the device, and linearity of the SiPM signal with light intensity is maintained as long as the number of optical photons interacting per SiPM cell is 1 or less. At high light intensities, which are opposite to this condition, the signal saturates, creating non-linearity at the incident light level. In the case of PET, nonlinearity is thereby created between the detector signal and the energy accumulated in the scintillator, and the ability to reject compton scattered annihilation photons, or the pile-up (pill-up) brought about by 2 annihilation photon pulses interacting simultaneously within the same scintillator element, is reduced based on energy loss.

Fig. 2A shows a plot of pulses resulting from the detection of gamma rays, with voltage plotted along the vertical axis and time plotted along the horizontal axis. Further, fig. 2A shows a predefined threshold of about 1.2 millivolts, over which the time the pulse exceeds is the TOT value. As shown in fig. 2B, the TOT value is monotonously related to the energy of the detected gamma ray, and can be represented by the area under the curve of the pulse or the amplitude of the pulse. For signals below the above threshold, the signal/detection is not shown.

In addition to the saturation nonlinearity and the TOT nonlinearity of the above-described exemplary detectors, there are other factors that sometimes produce nonlinearity in PET detectors. The calibration method described herein is generic regardless of the supply or type of non-linearity, and can be applied to all detector non-linearities regardless of the supply of non-linearity. That is, in the present specification, detector saturation and nonlinearity of TOT are used for illustrative purposes, but examples of nonlinearity in PET detection are not limited to these.

Errors caused by non-linearities can be exacerbated by multi-channel detection events, which occur when energy from a single gamma ray is shared among multiple detector elements and detected (e.g., compton scattering, optical cross-talk, etc.). However, by determining which detection event is a multi-channel event, and then identifying a multi-channel event group (e.g., events based on detection time, spatial proximity, and/or individual energy) resulting from the same gamma ray, the total energy can be recovered. Next, the total energy of the original gamma rays can be reconstructed by summing the energies measured from all the events generated by the same gamma ray and summing the shared energies. That is, the total energy of the gamma rays is determined by summing the energies from the detectors sharing the energy. That is, the calibration function is able to recover the total energy after performing energy calibration in the event of a multi-channel detection event, for example, due to compton scattering or optical crosstalk. When the original energy signals are summed without correcting the nonlinearity and the energy is measured as a single-channel detection event as shown in fig. 3, a different total energy value (for example, a larger value) is indicated.

In particular, FIG. 3 shows an example of the non-linearity resulting from TOT measurements, comparing single channel events to multi-channel detection events. Event 1 is a single channel detection event where 511keV gamma ray energy accumulates entirely on a single crystal. An energy window centered on the 511keV energy of the gamma ray caused by positron-electron annihilation is shown on the right. Events 2 and 3 correspond to 2-channel detection events where 2 crystals each detect a portion of the energy of a gamma ray totaling 511 keV.

In event 2, 171keV is detected in the first crystal and 340keV is detected in the second crystal (i.e., the aggregate energy is 171keV +340keV to 511 keV). In event 3, the first and second crystals absorb/detect energy at 255keV and 256keV, respectively. As shown on the right side of fig. 3, without nonlinear correction, the sum of the energy of event 2 and the energy of event 3 is outside the range of the specified energy window. Both events accumulate a total of 511keV, but since there is no nonlinear correction for the measured signal, the total signal is much larger than when single-channel detection of 511keV is registered, and these events are discarded, resulting in a decrease in sensitivity.

For example, it is not uncommon for 65% of the detection events of the scintillator and SiPM-based gamma-ray detectors to become single-crystal/single-channel detection events, and by compton scattering, 30% of the detected gamma rays become 2-channel detection events, and the other 5% become 3-channel detection events. In this case, if the multi-channel detection event is excluded, the single count rate is reduced to 65% efficiency, while the count rate is reduced to 42% efficiency. PET imaging relies on simultaneous detection to determine the line of response (LOR), and therefore, if a multi-channel detection event is excluded, the overall sensitivity is reduced by 50% or more.

The term "energy" used in the present specification is not limited to calibrated energy that is linearly related to actual energy or true energy. In general, the term "energy" as used in this specification means actual energy or real energy and specifies the energy coordinate associated therewith monotonically. Therefore, the term "energy" does not necessarily mean actual energy or real energy unless the context clearly indicates otherwise.

For example, in the case of the summation of energies described herein, the summation can be performed with respect to the "energy coordinate" and not with respect to the calibration value linearly associated with the actual energy. Measured/raw energy E_raw(i.e., "energy coordinates") can be related by a non-linear function E_true＝f(E_raw) With true energy E_trueEstablish associations, otherwise, can be in accordance with E_raw＝f^-1(E_true) The inverse function is applied to map to the original energy value measured from the true energy. Since the relationship between the measured raw energy and the true energy is non-linear, the 2 measured energies f from the 2-channel detection^-1(E₁) And f^-1(E₂) Is not equal to the measured/raw energy of an equivalent single-channel detection, i.e., f^-1(E₁)+f^-1(E₂)≠f^-1(E₁+E₂) Here, E₁+E₂＝E_TotalIn addition, E_TotalIs the true energy of incidence, e.g. 511 keV. Therefore, in order to accurately compare the multi-channel detected energy with the signal channel detection, energy calibration and correction are applied to each energy separately before summing the multi-channel detected energy.

As discussed above, the methods described herein can be better understood by comparison with related calibration methods that use a large number of individual sources and isotopes for energy calibration. For example, in these correlation methods for calibrating the nonlinearity, a plurality of isotopes are used for measurement in order to derive a plurality of spectral positions of energy covering a range to be used. Frequent replacement of multiple isotopes in a production or clinical setting is expensive, and the assay is time consuming and cumbersome, and thus is not desirable.

However, multi-source energy calibration is still useful in an initial (one time) calibration of the PET scanner (e.g., when the PET scanner is initially installed). Then, a shortened calibration procedure performed for updating the energy correction using the Lu-176 spectrum alone or in combination with spectra from other radioisotopes enables a subsequent (secondary) calibration. Since the initial/primary calibration is only 1 event (or at least a few events), the extra burden of a more complicated calibration process can be justified, whereas the recalibration/update of the energy calibration occurs more frequently, and it is important to minimize the time and effort required for recalibration.

To overcome the additional time and labor required for the calibration method for a large number of sources, the method described in this specification takes advantage of the fact that PET scanners can use lutetium-based scintillators to produce. The Lu-176 present in the scintillator provides background radiation (background radiation) that can be used in calibration and/or daily quality management. The use of the Lu-176 background spectrum in the energy non-linear calibration can replace the energy non-linear calibration of a procedure (routine) using multiple isotopes. FIG. 4 shows a plot of the background spectrum of Lu-176 as a function of corrected total energy. The Lu-176 background spectrum was observed to have a considerable structure. Thus, 1 method of non-linear calibration is to use a fitting method to determine the parameter values (some of which represent non-linearity) that give the best agreement between the measured spectrum and the parameterized model.

Fig. 5 shows a flow diagram of a method 100 with a first process of generating an energy calibration 115 and a second process of correcting raw data 105 using the energy calibration 115. Then, an image is reconstructed using the corrected data 155. The embodiment shown in fig. 5 is a non-limiting example of the calibration method described herein. Examples of a medical imaging mode (imaging modality) for reconstructing an image using the corrected data 155 include PET and SPECT images. That is, the gamma-ray radiation imaging apparatus may be 1 of a Positron Emission Tomography (PET) scanner and a Single Photon Emission Computed Tomography (SPECT) scanner. In another embodiment, the method 100 can be used for projection imaging, and in this case, the method 100 can omit step 170 and output a projection image based on the filtered correction data 155 in step 160. In 1 example of the medical imaging mode using the other embodiment, single photon emission using gamma rays is used to project an image. Also, projection imaging can be performed using an arbitrary gamma-ray source together with an array of gamma-ray detectors configured as a gamma camera.

In step 110, an energy calibration 115 is generated using the calibration data 103. To ensure that the energy calibration 115 is not an inferior decision, the number of spectral features provided by the calibration data 103 must be above the number of intrinsic parameters in the energy calibration 115. For example, the calibration function uses n spectral features of a reference spectrum corresponding to known energy as a reference value, optimizes the matching between the reference value and the calibrated energy, and the number of parameters for nonlinear energy correction is n or less of the number of spectral features. For example, the energy calibration 115 may be represented by equation (1) below.

[ numerical formula 1]

E＝f(x，p)＝α(β|+e^x/y)...(1)

In equation (1), E is the calibrated energy, x is the original energy signal (here, these are expressed as TOT values, which are a non-limiting example of the original energy signal), and p ═ α, β, γ is a parameter that defines the energy calibration 115. The parameter p defines an objective function (a least squares objective function, a log likelihood objective function, or the like), and the value of the parameter p after optimization can be solved by solving the objective function and achieving agreement between the calibrated energy obtained by using the energy calibration 115 and a known energy value of the spectral feature.

In one embodiment, step 110 is performed by finding the parameter p that solves the optimization problem. The parameter p can be represented by, for example, the following formula (2).

[ numerical formula 2]

In the formula (2), E_i ^(c)Is a known energy value of a spectral feature identified within the calibration spectrum of the calibration data 103. Furthermore, E_i ^(c)This is an example of a reference value of the reference spectrum. In addition, x_i ^(c)Is the original energy signal with respect to the spectral feature. For example, when the spectrum is characterized by peaks of 202keV and 307keV as shown in fig. 4, the original energy value in the calibration spectrum of Lu-176 is found for the maximum values corresponding to these 2 peaks, and the original energy signal can be obtained. When the spectral feature is an edge such as an edge of 597keV, the problem of deriving an original energy signal corresponding to the edge is slightly more complicated than determining the maximum value. That is, where the radioisotope is the lutetium isotope 176(Lu-176), the calibration function can use spectral characteristics including a202 keV peak and a307 keV peak for energy calibration. In addition, the calibration function can also use spectral features including the 597keV edge in the measured spectrum of Lu-176 to perform energy calibration.

Regarding the 597keV edge, in 1 method of determining the original energy signal related to the 597keV edge, a model of the physical matrix (described below in detail) is used to fit to the shape of the Lu-176 calibration spectrum for a range of values over a predetermined range of energies (e.g., energies in the range of 550keV to 1 MeV). As described below, 597keV corresponds to beta trace (beta replica). Further, while the β -rays 5 to 7 can contribute significantly in the range of 550keV to 1MeV, there is a possibility that the contribution of other β -rays is insignificant in this energy range. The spectral shape of these beta trails can be calculated beforehand if given values of spectral resolution that can be determined based on the peak shapes of 202keV and 307 keV. Next, the simulated spectrum can be calculated by adjusting the weighted sum of the pre-calculated spectral shapes of the β trace, transforming the summed spectra along the original energy signal axis, and achieving the best fit between the simulated spectrum and the Lu-176 calibration spectrum. The original energy signal at the 597keV edge is provided by the optimal position of the simulated spectrum along the axis of the original energy signal.

Another method can also be used to obtain the original energy signal corresponding to the third spectral feature. For example, a value between peaks may be used as the third spectral feature. Alternatively, the maxima near the 597keV edge can be used as the third spectral feature, or the third spectral feature can be set to a value near 800keV where the Lu-176 spectrum is reduced to half of the maximum peak near the 597keV edge.

In certain embodiments, rather than performing calibration using only a few discrete spectral functions within the Lu-176 calibration spectrum, the Lu-176 calibration spectrum can be used for calibration in its entirety. For example, if the non-linearity of the detector is calibrated, a histogram of counts as a function of energy can be saved to memory. Then, when the detector is recalibrated (e.g., over-time degradation and drift in detector performance), the stored histogram is recalled from the memory and can be compared with a new histogram of the Lu-176 calibration spectrum. By adjusting the parameters of the energy calibration 115 until the new calibrated histogram matches the old calibrated histogram, the energy calibration 115 can be periodically fine-tuned taking into account the time-dependent changes in the nonlinear detector response. That is, the calibration function can optimize the coincidence between the reference value and the energy calibrated based on the calibration data using an objective function that indicates the coincidence between the reference histogram of the reference spectrum and the spectrum generated by applying the nonlinear energy correction to the first energy spectrum.

From the above example, it can be seen that the energy calibration 115 can be generated from the calibration data 103 using several variations in step 110. In addition to the methods described above, a 4-parameter energy calibration 115 can be generated by augmenting the calibration data 103 to include spectra from other radioisotopes in addition to the Lu-176. For example, the calibration data 103 may include a spectrum from a germanium isotope (Ge-68) or a fluorine isotope 18 (F-18). For example, the obtaining function further obtains a second energy spectrum corresponding to either the germanium isotope 68(Ge-68) or the fluorine isotope 18(F-18) to obtain calibration data, and the calibration function adjusts parameters of the nonlinear energy correction to optimize coincidence of at least 2 spectral features associated with the first energy spectrum and optimize coincidence between the reference value and the calibration data regarding at least 1 spectral feature of the second energy spectrum, thereby enabling energy calibration. For example, the detection function detects when the energy calibration is performed by periodically checking the difference between the known values of the spectral features of 2 or more of the additional calibration data and the corrected energy. Here, the spectral feature may be a peak or a valley in the reference spectrum.

Fig. 6 shows an example of an energy calibration model for the correction of non-linearities in the time of threshold (TOT) technique, where 6 spectral features are used for curve fitting. In this case, the functional form selected so as to fit to the data can be represented by, for example, the following equation (3).

[ numerical formula 3]

E＝α(β+e^x/y)...(3)

The method 100 is not limited to an energy calibration model having the specific functional form assigned above. In other embodiments, the target data can take other forms without departing from the spirit of the embodiments described above.

As mentioned above, step 110 can also be performed using a multi-source calibration method, and additionally, in step 140, the energy calibration 115 is performed using the method described above, where only the Lu-176 calibration spectrum is used, or the maximum Lu-176 calibration spectrum is used in combination with the other spectra of 1 radioisotope.

In the case of using multi-source calibration in step 110, calibration can be performed using the following spectrum from the source. Namely, (i) Am-241 (peak at 59.5keV), (ii) Ba-133 (peak at 81keV and 356keV), (iii) Co-57 (peak at 122keV), (iv) Lu-176 (peak at 202keV and 307keV), (v) Ge-68 (peak at 511keV), and (vi) Cs-137 (peak at 662 keV). These isotopes are selected to cover the range of interest of 511keV gamma rays and their compton scattering interactions. In the multi-source method of energy calibration, the parameters p of the energy calibration model f are generated by curve fitting the TOT values corresponding to the 8 energy peaks to known energies (59.5, 81, 122, 202, 307, 356, 511, and 662keV) for the isotopes.

In step 120, using the new calibration data 113, including the Lu-176 spectrum, it is monitored whether the non-linear response of the imparted detector has changed sufficiently to the extent that recalibration is desired. For example, new calibration data 113 can be accumulated at any time when the detector is not being used for imaging (i.e., in an invalid (idling) state). That is, the acquisition function acquires a count from background radiation emitted from the scintillator crystal during a time between a plurality of imaging scans in which the gamma ray radiation imaging apparatus is not used for imaging, the count acquired by the acquisition function being calibration data used for energy calibration. The energy calibration 115 can then be applied to the new calibration data 113, and 1 calibrated energy value in the spectral signature in the Lu-176 spectrum can be derived from the energy corrected Lu-176 spectrum. If the spectral feature of the monitored object is a307 keV peak, a corrected energy value for a maximum corresponding to the 307keV peak can be determined. If the corrected energy value exceeds a predetermined threshold and is different from a known value (i.e., 307keV), then in step 130 the method 100 notifies the energy correction that there is a drift, i.e., "yes," and the method 100 proceeds to step 140 for updating the energy calibration 115. That is, the gamma-ray radiation imaging apparatus further includes a detection function for detecting when to update the energy calibration, wherein the detection function acquires additional calibration data using the radioisotope after the energy calibration, applies the energy calibration to the additional calibration data, determines corrected energies of 2 or more spectral features in the additional calibration data, and can transmit a signal for updating the energy calibration when a difference between a known value of the 2 or more spectral features and the corrected energy of the spectral feature determined from the additional calibration data satisfies 1 or more recalibration reference. Otherwise, the method 100 continues to monitor new calibration data 113 taken during image acquisition.

To avoid the possibility that 1 measurement including noise unnecessarily updates the calibration, the reference used in step 130 can be based on a moving average, or n of the m correction energy values most recently generated in step 120, as differing from known values by an amount greater than a predefined threshold. For example, if 3 out of 5 of step 120 results in a result outside the predetermined threshold, the process proceeds to step 140 to update the calibration.

In certain embodiments, step 120 can monitor the corrected energy values of the plurality of spectral features. With respect to linear energy correction, it is sufficient to monitor only a single spectral feature, but with respect to nonlinear energy correction, even if the corrected energy of a certain spectral feature is correct, there may be a case where the corrected energy of another spectral feature becomes incorrect. Therefore, in step 120, the corrected energy values of 2 or more spectral features can be monitored. In addition/instead, step 120 can monitor the difference between the corrected energy values relating to the 2 spectral features.

In step 140, the energy calibration 115 is recalibrated using the method described above based on the Lu-176 spectrum and the spectrum of up to 1 other radioisotope.

In step 150, the energy calibration 115 is applied to the raw data 105, generating corrected data 155. For example, the parameter p is applied as an input to the energy calibration model f together with the energy signal x (also referred to as an energy coordinate), and a calibrated energy value E is generated. The calibrated energy value E can be represented by, for example, the following equation (4).

[ numerical formula 4]

In general, the shape of the nonlinear response, although somewhat different, is similar across detector elements/channels. To cope with these variations, the above parameters can be calibrated for each readout channel/module.

As described above, the energy calibration model f is not limited to the functional form of the following expression (5).

[ numerical formula 5]

For example, instead of parameterization in the form of a function, parameterization may be represented using parameters of a look-up table (LUT). In one embodiment, for example, the LUT can map E_i＝f(x_i) The discrete points of (2) are correlated, and a mapping of points between the discrete points can be determined by interpolation.

Thus, in certain embodiments, rather than using a functional form, a LUT can be used to specify the correction for the non-linearity. In this case, the LUT specifies a correction coefficient corresponding to a specific signal level. The correction coefficient relating to the signal level not displayed in the LUT can be decided by interpolation or extrapolation from the value displayed in the LUT. Depending on the constraints of accuracy and computational complexity, different interpolation methods (e.g., spline, linear, or cubic spline) can be used. Similarly, the number of signal levels of the LUT depends on constraints on accuracy and computational complexity. Generally, the greater the number of values, the greater the energy resolution (i.e., the accuracy of the correction). In the case of the LUT method, all correction coefficients of the LUT may be respective parameters determined by energy calibration.

As mentioned above, the energy calibration can include, but is not limited to, a TOT nonlinear correction. In addition to this, the energy calibration can account for non-linearities due to charge sharing, thresholding, and other non-linear effects. Furthermore, the energy calibration may be a lookup table indexed by the position/Identity (ID) of each detector element in order to obtain the parameters of the equation representing the nonlinear correction. Thus, the parameterization of the energy calibration can be performed on the detector element substrate with respect to the detector elements.

In step 160, an energy window is applied to the corrected data to remove random coincidences, thereby improving image quality. For example, in PET imaging, the energy window reaches an energy of 511keV corresponding to positron annihilation. As described above, in the specific embodiment, it is determined which detection event corresponds to a multichannel event, the energies of the respective multichannel detection events are summed, and the total energy of the respective multichannel detection events is determined, whereby the multichannel detection can be repaired. That is, the gamma-ray radiation imaging apparatus may include a generation unit that acquires radiation data from a medical imaging scan using the gamma-ray imaging apparatus, filters the radiation data so as to omit counting of corrected energy values existing outside an energy window up to 511keV, and reconstructs a tomographic image using the filtered radiation data. Details of 1 embodiment of this process are shown below.

In step 170, the PET image 155 is reconstructed from the correct PET data using known reconstruction methods. For example, an image of radioactivity levels (e.g., tracer density) can be reconstructed as a function of voxel location using the PET data 145. As will be appreciated by those skilled in the art, the image reconstruction may be performed using a backprojection method, a filtered backprojection method, a fourier transform-based image reconstruction method, an iterative image reconstruction method, a matrix inversion image reconstruction method, a statistical image reconstruction method, a list mode method, or other reconstruction methods, or a combination thereof. For example, the lPET image 175 may be reconstructed using an initialized Subset extension visualization (OS-EM) algorithm based on the Subset-based Expectation Maximization algorithm initialized in the FBP reconstructed PET image.

Returning to step 160. The raw data 105 can include energy, time, and location corresponding to a gamma ray detection event. For example, the detection event may correspond to a pair of gamma rays emitted at a positron annihilation event generated by the subject OBJ. The detection event can be detected by any one of a plurality of detector elements. When multi-channel detection occurs, the energy from 1 gamma ray is detected dispersedly by 2 or more detector elements. The 2 or more detector elements can be located within a single detector module (e.g., adjacent detector elements) or can be assigned to 2 or more detector modules. For example, in compton scattering, scattered gamma rays pass through several detector elements before being absorbed by a second detector element that is farther away from the first detector element where compton scattering occurs.

In step 160, multi-channel detection events are identified from the energy correction data 155, and the identified multi-channel detection events are then grouped per event. I.e., each group corresponds to a single primary gamma ray. In the case of primary scattering, 2 collisions (hit) are included in each group. One is the energy detected by the first crystal that undergoes compton scattering, and the other 1 is the energy detected by the second crystal that absorbs the scattered gamma rays via photoelectric absorption. Likewise, in each group of 2 scatter events, 3 collisions (i.e., 1 in one gamma ray, 2 for each of the 2 scattered gamma rays) are included, etc. (e.g., 4 collisions in the group corresponding to 3 scatterings).

The multichannel event can be selected based on, for example, the temporal proximity of the detection signals, the spatial proximity of the detection signals, the aggregate of the energies of the signals, or any combination thereof. For example, where the gamma ray source has a known energy (e.g., 511keV for gamma rays from positron annihilations), the closer the signal is to the known energy, the higher the likelihood of corresponding to the same multi-channel event. Furthermore, signals generated in close temporal proximity are highly likely to correspond to the same multi-channel event, and signals generated in close spatial proximity are highly likely to correspond to the same multi-channel event. Further, if all of the 3 conditions (energy, time, space) are satisfied, the probability that the signal corresponds to the same multi-channel event is further increased. Thus, a process of grouping the above-described signals into multi-channel events using multivariate statistical analysis can be performed.

As described above, in certain embodiments, the energy signal values for various spectral features can be determined using a model of the physical matrix of the spectrum. In this method, the input spectrum is fitted to a complex spectral model of the physical matrix. The model includes adjustable parameters that account for non-linearities. For best fitting of the data, non-linear coefficients are provided among other parameters.

FIG. 7 shows a level diagram (level diagram) of the Lu-176 energy level and the emission path. As shown in FIG. 7, Lu-176 attenuates through the cascade of beta radiation and its subsequent gamma rays. The beta radiation and gamma cascade occur essentially simultaneously (i.e., at times that are far closer in time than the resolution of the detector system). Fig. 8 shows a case where the Lu-176 spectrum can be modeled as an overlap of spectra from a plurality of radiation attenuation processes based on a physical model.

Considering fig. 7 and 8, the model of the physical substrate can be better understood by considering several simplified assumptions. First, in the β radiation, it can be assumed that all attenuation occurs in a path of 99.1%.

Second, the beta energy is essentially always fully captured by the scintillator. Therefore, it can be assumed that 100% of the β particles store all of these energies in the crystals they produce.

Third, gamma rays (88keV, 202keV and 307keV) are likely to be captured or escape (escape). The probability depends on the energy and the size of the scintillator. As a result, the β spectrum is copied several times, and the entire spectrum becomes the sum of these copied spectra. For example, decay of captured gamma rays at 88keV and 307keV generates a shifted (88+ 307-395 keV) beta spectrum. Therefore, the probability of escape of each of the 3 gamma rays can be represented by 3 probabilities (P88, P202, P307), where P88 is the probability of escape of a gamma ray of 88keV, P202 is the probability of escape of a gamma ray of 202keV, P307 is the probability of escape of a gamma ray of 307keV, and P88 < P202 < P307. In reality, the probability of escape is assumed to depend on the place where the radiation attenuation occurs within the scintillator, and on the other hand is not a function of the position and geometry of the above-mentioned crystal but a constant, and is therefore simplified.

[ Table 1]

Table 1: probability of 8 beta trace scenes (beta replica scenarios)

Fourth, there are 2 for 3 gamma rays (each escaping or capable of being captured)³8 possible beta spectrum trace lines (beta spectra replicas). The probability of 3 gamma rays escaping or not escaping for each of the 8 possible rearrangements is shown in table 1. The beta-dash numbers on the left side correspond to the beta-dash numbers shown in fig. 8. For example, when gamma rays of both 88keV and 202keV are captured, the spectrum shifts (i.e., 88+202 ═ 290keV), as shown in fig. 8, providing a spectrum of beta trace 5. The probability (weight) assigned to the scene is given by (1-P88) × (1-P202) × P307.

Fifth, when there are other scintillator elements (other scintillator pixels within the same detector module, other detectors within the PET detector ring, etc.), the other detectors detect the exit from 1 detector (mainly 202 and 307keV), resulting in additional peaks. To simplify the above physical model, it can be assumed that the 202keV and 307keV escapes from the other detector elements only contribute significantly to the spectrum as a whole.

Sixth, the spectral characteristics are changed according to the energy resolution of the entire detection system (combination of the scintillator, the photosensor, and the electronic device). In order to simplify the physical model, it can be assumed that the energy resolution can be described by a single parameter. For example, the resolution at 511keV can be used as 1 parameter. Hereinafter, the resolution at 511keV is expressed as the following formula (6).

[ numerical formula 6]

In addition, it can be assumed that the energy resolution at a specific energy E is given by the following equation, and further, it can be assumed that the energy resolution in the given energy E is given by the following equation (7).

[ number formula 7]

In the above, it is assumed that the energy resolution is proportional to the square root of the energy.

Each beta trace can be represented by a parameterized shape function B. For example, the shape function B can be represented by the following expression (8).

[ number formula 8]

In formula (8), E₀₀Is the energy shift of the trace of the gamma ray that is absorbed simultaneously. The amplitude is determined by an amplitude scaling coefficient a and a relative amplitude coefficient given to the right-hand column of table 1. As an example 1, for trace lines representing line 6 of Table 1 (i.e., gamma ray energies 88keV and 307keV are captured), the energy shift is E₀₀88+307 395keV, 6 beta for beta trace₆The overall trace of 6 can be represented by the following formula (9).

[ numerical formula 9]

As shown in fig. 8, the overall energy spectrum is the sum of 8 beta-axis and 2 gamma peaks (202keV and 307keV) caused by the escaped absorption from other detector elements. Additional equations can be applied to model the effect of the link along the detection path (e.g., scintillator, photosensor, readout electronics). For example, the quantum efficiency of a photosensor may vary as a function of energy.

The non-linearity of the detector is then represented by a parameterized equation. For example, in the readout of a detector using the time-of-flight (TOT) method exceeding a threshold, the nonlinearity can be described using 4 parameters (C, a, E0, and ToT 511). The energy E is expressed by equations (10) to (12) below, for example.

[ numerical formula 10]

d＝E0/C-1...(10)

[ numerical formula 11]

b＝ToT511/log(511/C-d)...(11)

[ numerical formula 12]

E＝C*(exp(ToT/(a/|ToT+b))+d)...(12

Here, ToT is a measured signal.

When all the above assumptions are summarized, the entire Lu-176 spectrum is described by a physical model having 11 free parameters.

1A: integral scale factor of beta trace

2 Eres _ 511: energy resolution at 511keV

3P 88: escape probability of 88keV

4P 202: escape probability of 202keV

5P 307: escape probability of 307keV

6A 202: amplitude of the 202keV peak (escape from other detectors)

7A 307: amplitude of 307keV peak (escape from other detectors)

8C: nonlinear parameter #1

9 a: nonlinear parameter #2

10E 0: nonlinear parameter #3

11 ToT 511: nonlinear parameter #4

In case only the energy signal value corresponding to the edge of 597keV becomes problematic, the number of free parameters can be reduced. For example, the values of A202 and A307 may be ignored, and the values Eres _511 may be taken from the peaks of 202keV and 307 keV. Furthermore, instead of fitting 4 parameters, the ToT values (ToT) can be scaled and transformed with respect to the energy value E using a 2-parameter fit, reducing the number of free parameters to 6. These 6 parameters can be determined by fitting methods known to those skilled in the art. For example, it can be decided using a simplex search, or it can be decided using a least squares penalty function.

The above-described implementation of the method 100 is primarily represented using the Lu-176 frequency spectrum. The Lu-176 spectrum represents a number of discrete and continuous spectral features, thus facilitating nonlinear energy calibration. However, spectra derived from a single peak spectrum like Ge-68 can also generate discrete and continuous spectral features by compton scattering and other physical processes within the detector crystal. That is, as a practical matter, the spectrum of the radiation absorbed by the detector may have additional features (e.g., back scatter peaks and compton edges) beyond the emission spectrum features of the radioisotope. These additional features can include a compton back-scattered peak, a compton edge, and various escape peaks, as shown in fig. 10, 11A, and 11B. In this way, radioisotopes having only a single emission energy can be used to calibrate multiple spectral features, and thus nonlinear energy calibration can be performed. For example, the calibration function can perform energy calibration using spectral features including a radiation peak of radiation energy emitted by the radioisotope and 1 or more backscatter peaks and compton edges.

FIG. 10 shows a diagrammatic view of various physical processes generated by a single energy source. Here, Ge-68 emits gamma rays at 511 keV. However, it is shown that back-scattering from crystals located in the upper part of the ring is absorbed by the detector in the region below and to the left of the ring. Further, fig. 10 shows the gamma ray energy absorbed by multi-compton scattering and the energy absorbed in the presence of X-ray escape.

FIG. 11A shows an absorption spectrum resulting from the detection of 511keV gamma rays emanating from Ge-68, including various spectral features corresponding to the scattering process described above. In fig. 11A, a logarithmic scale is used in the vertical direction, and the absorption spectrum is represented on the assumption of full detector resolution in order to better analyze various functions. In FIG. 11B, a linear scale is used for the vertical axis, assuming a defined detector resolution. Ge-68 emits gamma rays having only a single energy, but the absorbed energy shows more distinct spectral features (e.g., based on back-scatter peaks and compton edges). Therefore, even in the case where the radiation source radiates radiation of only a single energy, the above-described detection process may bring about many spectral features.

From this viewpoint, the method described herein can be used using a spectrum from a radiation source that emits radiation at a single energy, such as Ge-68, in addition to a radiation source that emits radiation at a plurality of energies, such as Lu-176.

The models of the physical matrix for the various scattering processes shown in fig. 10, 11A and 11B can be applied using known analysis and numerical representations relating to the scattering processes. Therefore, either the DL network implementation or the model implementation of the physical matrix can be applied to the absorption spectrum obtained from a radiation source emitting only 1 or 2 energies.

Specific implementations have been described, but these implementations are presented as examples only and are not intended to limit the teachings of the present disclosure. Indeed, the novel methods, apparatus and systems described herein can be implemented in various other ways. Furthermore, various omissions, substitutions, and changes in the form of the methods, apparatus, and systems described herein may be made without departing from the spirit of the disclosure.

According to at least 1 embodiment described above, the calibration of energy can be performed with high accuracy.

Several embodiments have been described, but these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in other various manners, and various omissions, substitutions, changes, and combinations of the embodiments can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalent scope thereof.

Description of the reference numerals

200 PET scanner

270 processing circuit

Claims (14)

1. A gamma ray radiation imaging apparatus is provided with:

an acquisition unit that acquires calibration data of radiation incident on a detector, the calibration data including a first spectrum collected when radiation from a radioisotope in a scintillator crystal is irradiated onto the detector; and

and a calibration unit configured to apply the energy signal measured by the detector to a nonlinear energy correction, and adjust a parameter of the nonlinear energy correction so as to optimize a matching between a reference value of a reference spectrum indicating an absorbed radiation energy and an energy after calibration generated when the first energy spectrum of the calibration data is applied to the nonlinear energy correction.

2. The gamma-ray radiographic imaging apparatus of claim 1, where,

the acquisition unit acquires a count from background radiation emitted from the scintillator crystal at a time between a plurality of imaging scans in which the gamma-ray radiation imaging apparatus is not used for imaging,

the count acquired by the acquisition unit is the calibration data used for the energy calibration.

3. The gamma-ray radiographic imaging apparatus of claim 1 or 2,

the calibration unit optimizes matching between the reference value and the calibrated energy by using n spectral features of the reference spectrum corresponding to known energies as the reference value,

the number of parameters of the nonlinear energy correction is n or less, which is the number of the spectral features.

4. The gamma-ray radiographic imaging apparatus of claim 3, where,

the radioisotope is the lutetium isotope 176(Lu-176),

the calibration section performs the energy calibration using the spectral characteristics including a peak at 202keV and a peak at 307 keV.

5. The gamma-ray radiographic imaging apparatus of claim 4, where,

the calibration unit also performs energy calibration using spectral features including the 597keV edge in the measured energy spectrum of Lu-176.

6. The gamma-ray radiographic imaging apparatus of any one of claims 3 to 5, wherein,

the calibration unit performs the energy calibration using spectral characteristics including a radiation peak of the radiation energy emitted from the radioisotope, 1 or more backscatter peaks, and a compton edge.

7. The gamma-ray radiographic imaging apparatus of claim 1, where,

the calibration unit optimizes matching between the reference value and the calibrated energy calibrated based on the calibration data using an objective function that indicates matching between a reference histogram of the reference spectrum and a spectrum generated by applying the nonlinear energy correction to the first energy spectrum.

8. The gamma-ray radiographic imaging apparatus of any one of claims 1 to 7,

the acquisition unit further acquires a second spectrum corresponding to either the germanium isotope 68(Ge-68) or the fluorine isotope 18(F-18) to thereby acquire the calibration data,

the calibration unit adjusts the parameter of the nonlinear energy calibration to optimize matching of at least 2 spectral features related to the first spectrum, and performs the energy calibration by optimizing matching between the reference value and calibration data of at least 1 spectral feature related to the second spectrum.

9. The gamma-ray radiographic imaging apparatus of any one of claims 1 to 8,

further comprising a detection section that detects when the energy calibration is updated,

the detection unit acquires additional calibration data using the radioisotope after the energy calibration, applies the energy calibration to the additional calibration data, and determines corrected energies of 2 or more spectral features in the additional calibration data,

transmitting a signal for updating the energy calibration when a difference between the known values of the 2 or more spectral features and the determined corrected energy of the spectral features, which is determined based on the spectral features determined from the additional calibration data, satisfies 1 or more recalibration criteria.

10. The gamma-ray radiographic imaging apparatus of claim 9, where,

the detection unit periodically checks the difference between the known values of the 2 or more spectral features in the additional calibration data and the corrected energy, thereby detecting when to update the energy calibration,

the spectral feature is a peak or a valley in the reference spectrum.

11. The gamma-ray radiographic imaging apparatus of any one of claims 1 to 10, wherein,

radiation data is acquired from a medical imaging scan using a gamma-ray imaging apparatus,

in order to omit counting of corrected energy values lying outside the energy window up to 511keV, the emission data is filtered,

the gamma-ray radiographic imaging device further includes a generation unit that reconstructs a tomographic image using the filtered radiation data.

12. The gamma-ray radiographic imaging apparatus of claim 11, where,

the gamma ray imaging device is 1 of a Positron Emission Tomography (PET) scanner and a Single Photon Emission Computed Tomography (SPECT) scanner.

13. The gamma-ray radiographic imaging apparatus of any one of claims 1 to 12, wherein,

the calibration unit recovers the total energy after performing the energy calibration when a multichannel detection event occurs due to compton scattering or optical crosstalk.

14. An energy calibration method, comprising the following processes:

acquiring calibration data of radiation incident on a detector of a gamma-ray imaging apparatus, the calibration data including a first spectrum collected when radiation from a radioisotope in a scintillator crystal is irradiated to the detector; and

applying a non-linear energy correction, adjusting parameters of the non-linear energy correction to optimize a correspondence between a baseline value of a baseline spectrum representing absorbed radiant energy and a calibrated energy generated when the first spectrum of the calibration data is applied to the non-linear energy correction, thereby performing an energy calibration that corrects the energy signal measured by the detector.

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