A method and system for channel gain correction of a multi-channel detector

Abstract

This invention discloses a channel gain correction method and system for a multi-channel detector, relating to the field of nuclear radiation detection technology. The method includes: acquiring raw event data using a multi-channel detector; determining the energy of all channels triggered by the same event based on the action time; calculating the total energy value for each event using the energy of its respective channels, statistically analyzing all events to construct the total energy spectrum of the channel events, and setting an energy filtering window based on the position of the photoelectric peak in the total energy spectrum, retaining events within the energy filtering window as the valid event set; using an iterative algorithm to correct the channel gain coefficients, and monitoring the energy resolution in real time until convergence; applying the finally obtained gain coefficients to perform channel-level gain compensation on all raw event data, and outputting and displaying the corrected energy spectrum and gain distribution map. Gain correction can significantly improve the energy resolution and position resolution of the detector, enhancing the system's imaging quality and quantitative accuracy.

Description

Technical Field

[0001] This invention relates to the field of nuclear radiation detection technology, and in particular to a channel gain correction method and system for a multi-channel detector. Background Technology

[0002] Molecular imaging is the science that uses imaging techniques to visualize specific molecules at the tissue, cellular, and subcellular levels, reflecting changes at the molecular level in living organisms and conducting qualitative and quantitative studies on their biological behavior through imaging. Commonly used molecular imaging techniques include: Computed Tomography (CT), Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT), and Magnetic Resonance Imaging (MRI).

[0003] Detectors using continuous crystal coupled silicon photomultiplier tube (SiPM) arrays are widely used in PET and SPECT due to their high spatial resolution and high detection efficiency. However, the non-uniformity of channel gain in SiPM arrays during data transmission can affect the accuracy of data results and significantly interfere with the actual performance of the detector. This non-uniformity leads to broadening of the energy spectrum received by the detector, resulting in decreased positioning accuracy and affecting the accuracy of the detection results.

[0004] The signal readout mechanism of continuous crystal detectors is extremely sensitive to the uniformity of channel gain. The photon distribution of a single gamma event typically covers multiple SiPM channels, and gain differences between adjacent channels distort the calculation of the centroid of the light intensity distribution. Studies have shown that when the channel gain difference exceeds 5%, the positioning error in the crystal edge region can reach 1.8 mm. Furthermore, the temperature dependence and aging effects of SiPM devices further exacerbate gain inconsistencies. The limitations of traditional offline calibration methods in terms of real-time performance and dynamic compensation capabilities further complicate this problem.

[0005] Currently, some research has made progress in hardware compensation and algorithm optimization. One existing approach is an online gain calibration system based on pulsed LED light sources, which integrates a miniature LED array within the detector module to achieve real-time gain monitoring. However, this is limited by the calibration cycle interruption time, making it difficult to meet the needs of dynamic imaging. Another approach is gain correction methods based on deep reinforcement learning (DRL) or convolutional neural networks (CNN), but model deployment requires significant resources, hindering clinical translation.

[0006] Therefore, there is an urgent need for a data-driven gain correction method that is simpler and less costly than traditional gain correction methods. Summary of the Invention

[0007] To address the aforementioned problems, the present invention aims to provide a channel gain correction method and system for multi-channel detectors, applicable to radiation detection systems employing scintillator arrays and multi-channel readout structures, such as PET (positron emission tomography) or SPECT (single-photon emission computed tomography) imaging systems, to solve the problem of decreased detection accuracy caused by non-uniformity in multi-channel gain response. The technical solution is as follows:

[0008] On the one hand, a method for channel gain correction of a multi-channel detector is provided, the method comprising the following steps:

[0009] S1. Use a multi-channel detector to collect raw event data, wherein the event is a gamma photon event, and the raw event data includes the charge value and duration of each channel in the event;

[0010] S2. Determine the energy of all channels triggered by the same event by judging the action time;

[0011] S3. Calculate the total energy value for each event through the energy of each channel, count all detected events, construct the total energy spectrum of the channel events, and set an energy filtering window according to the position of the photoelectric peak in the total energy spectrum. Remove events outside the energy filtering window and retain events inside the energy filtering window as the set of valid events.

[0012] S4. Use an iterative algorithm to correct the channel gain coefficients. Initialize the channel gain coefficients. Each iteration includes: calculating the total energy of effective events for each channel according to the current gain coefficient; updating and correcting the gain coefficients of each channel based on the difference between the calculated total energy and the preset target energy; and monitoring the energy resolution in real time until convergence.

[0013] S5. Apply the final obtained gain coefficient to perform channel-level gain compensation on all original event data, and output and display the corrected energy spectrum and gain distribution map.

[0014] Optionally, the iterative algorithm constructs an objective function based on the principle of minimum error. By comparing the difference between the total energy calculated for each event in the current iteration and the preset target energy, the residual is allocated by channel and the correction amount of the gain coefficient of each channel is calculated in reverse to update the current gain coefficient until the residual converges or the number of iterations reaches the preset upper limit.

[0015] Optionally, in the iterative algorithm, the first... k The gain coefficient of each channel is calculated as follows:

[0016]

[0017] Where, p i The target energy value is dynamically adjusted. The current gain coefficient is used as the energy prediction value, and the deviation between the predicted total energy and the target energy value is used as feedback. For the first i The first event k The charge response of the channel is expressed as an energy value, f. k (n+1) f is the gain coefficient for the iteration. k (n) is the current gain coefficient, and N is the total number of events.

[0018] Optionally, the target energy value is calculated as follows:

[0019]

[0020] in, The energy represented by the position of the photoelectric peak in the total energy spectrum. The actual total energy value of all channels for the current event, where α is the compression factor; and the energy prediction value of the current gain factor. The calculation method is as follows:

[0021]

[0022] Where K is the total number of channels.

[0023] Optionally, the real-time monitoring of energy resolution until convergence specifically includes:

[0024] The iteration stops when the energy resolution change is less than the threshold δ after N consecutive iterations, where δ < 0.1%; or when the number of iterations reaches the preset number of iterations M.

[0025] Optionally, the method further includes evaluating the gain correction effect:

[0026] Generate theoretical gain distribution map and corrected gain distribution map, calculate the Pearson correlation coefficient between theoretical gain and corrected gain, and plot the curve of energy resolution change with the number of iterations to determine the relative gain difference between the detector channels.

[0027] Optionally, the calculation of the energy resolution includes:

[0028] Gaussian fitting was performed on the total energy spectrum according to the formula. Calculate the energy resolution, where σ is the standard deviation of the Gaussian fit of the total energy spectrum curve.

[0029] On the other hand, a channel gain correction system for a multi-channel detector is provided for implementing the method described in any of the above claims, the system comprising:

[0030] The data acquisition module is used to acquire raw event data using a multi-channel detector. The event is a gamma photon event, and the raw event data includes the charge value and duration of each channel in the event.

[0031] The preprocessing module is used to preprocess the collected raw event data, remove noise interference, and obtain the energy of all channels triggered by the same event by determining the action time.

[0032] The filtering module is used to calculate the total energy value of each event through the energy of its respective channels, count all detected events, construct the total energy spectrum of the channel events, and set an energy filtering window according to the position of the photoelectric peak in the total energy spectrum to remove events outside the energy filtering window and retain events inside the energy filtering window as the set of valid events.

[0033] The calibration module is used to perform channel gain coefficient calibration using an iterative algorithm. It initializes the channel gain coefficients, and each iteration includes: calculating the total energy of effective events for each channel based on the current gain coefficient, updating and calibrating the gain coefficients of each channel based on the difference between the calculated total energy and the preset target energy, and monitoring the energy resolution in real time until convergence.

[0034] The output and visualization module is used to apply the final obtained gain coefficients to perform channel-level gain compensation on all raw event data, and output and display the corrected energy spectrum and gain distribution map.

[0035] On the other hand, an electronic device is provided, the electronic device comprising:

[0036] processor;

[0037] A memory storing computer-readable instructions, which, when loaded and executed by the processor, implement the steps of the channel gain correction method for the multi-channel detector described above.

[0038] On the other hand, a computer-readable storage medium is provided, wherein program code is stored therein, the program code being invoked by a processor to perform the steps of the channel gain correction method for the multi-channel detector described above.

[0039] The beneficial effects of the technical solution provided by this invention include at least the following:

[0040] In this embodiment of the invention, raw event data is collected using a detector, and the gain coefficient of each channel is calibrated; the channel energy data of each event is extracted, the total energy of the event is calculated, and the photoelectric peak is located; valid events are selected by setting an energy filtering window; and the correction gain coefficient is updated by an iterative algorithm to concentrate the data near the photoelectric peak. The correction effect is observed by the change in energy resolution.

[0041] This method enables multi-channel gain estimation and compensation, significantly improving energy spectrum quality and system energy resolution; it requires no hardware modification and can be directly applied to data correction of existing detectors; it requires no additional hardware setup and achieves accurate compensation for inconsistent channel responses; it exhibits good robustness and is suitable for scenarios with systematic errors in practical detection systems; the algorithm has a clear structure and high computational efficiency; after gain correction, it can significantly improve the energy resolution and position resolution of the detector, enhancing system imaging quality and quantitative accuracy.

[0042] In summary, this invention provides an effective technical solution for gain consistency calibration of high-performance PET detectors, gamma imaging systems, and high-end medical instruments. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart of a channel gain correction method for a multi-channel detector provided in an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram illustrating the implementation process of a channel gain correction method for a multi-channel detector provided in an embodiment of the present invention;

[0046] Figure 3 This is a schematic diagram of the scintillation crystal detector provided in an embodiment of the present invention;

[0047] Figure 4 This is the energy spectrum obtained by summing and statistically analyzing the initial energy data obtained in the embodiments of the present invention with the full-channel signal;

[0048] Figure 5 This is the energy spectrum diagram after perturbation provided in the embodiment of the present invention;

[0049] Figure 6 This is the energy spectrum obtained by summing and statistically analyzing the energy data after iteration using the MLEM algorithm and the full-channel signal, as provided in this embodiment of the invention.

[0050] Figure 7 This is a graph showing the change in energy resolution with the number of iterations provided in an embodiment of the present invention;

[0051] Figure 8 (a) and (b) are the gain distribution diagrams before and after correction provided in the embodiments of the present invention, respectively;

[0052] Figure 9 This is a gain correlation analysis diagram provided in an embodiment of the present invention;

[0053] Figure 10 This is a schematic diagram of the channel gain correction system for a multi-channel detector provided in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0055] In embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the term "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either one or the other. In embodiments of the present invention, "image" and "picture" may sometimes be used interchangeably, and it should be noted that their intended meanings are consistent unless the distinction is emphasized. In embodiments of the present invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1, and their intended meanings are consistent unless the distinction is emphasized.

[0056] This invention provides a channel gain correction method for a multi-channel detector. The method is based on the maximum likelihood expectation maximization (MLEM) algorithm to estimate and optimize the channel gain channel by channel, and uses global energy resolution as the correction performance index. Finally, it outputs calibration parameters and evaluation reports for gain compensation.

[0057] refer to Figure 1 and Figure 2 As shown, the method includes the following steps:

[0058] S1. Use a multi-channel detector to collect raw event data. The event is a gamma photon event. The raw event data includes the charge value and duration of each channel in the event.

[0059] The detector structure model used in the embodiments of the present invention is as follows: Figure 3 As shown, in order to obtain ideal test data received by the detector under ideal conditions (uniform gain), the simulation data source in this embodiment uses the GATE (Geant4 Application for Tomographic Emission) simulation tool to construct a Monte Carlo photon transport model. This platform achieves full-process control of detector geometric modeling, physical process settings, and data acquisition through a script-driven approach. The specific implementation process of this embodiment can be broken down into five parts: system initialization, detector module geometric modeling, physical processes and test parameter settings, radiation source configuration, and data output.

[0060] The main structure of the detector module's geometric modeling consists of a 50×50×6 mm³ LaBr3 crystal block, a 0.5 mm thick epoxy resin coupling layer, and a 16×16 SiPM array. The crystal surface employs a bidirectional reflection model, with a 95% reflectivity TiO2 coating on the bottom and a 98% reflectivity polytetrafluoroethylene film on the sides. The SiPM units are 3×3 mm² in size and configured for 256 independent signal acquisition channels. The physical processes are designed to activate the photoelectric effect, Compton scattering, and pair production of gamma rays to achieve the physical effects observed in a real detection process. The radiation source uses... 22 The Na point source emits 140 keV gamma rays. The source term is located 50 mm directly in front of the crystal, with an activity defined as 10000 Bq, corresponding to the emission of 10 gamma rays per second. 4 One gamma photon. To simulate the actual detection environment, a lead collimator (3 mm aperture, 20 mm thickness) was set up and spatially positioned to face the detector crystal.

[0061] S2. Determine the energy of all channels triggered by the same event by judging the action time.

[0062] In this embodiment of the invention, raw event data after QDC quantization is read from a multi-channel detector and preprocessed, such as noise removal. Each event contains energy values ​​(corresponding to charge values) for 256 channels, based on its duration, and the data is stored in a binary file in 32-bit integer format.

[0063] S3. Calculate the total energy value for each event through the energy of its respective channels, count all detected events, construct the total energy spectrum of the channel events, and set an energy filtering window according to the position of the photoelectric peak in the total energy spectrum. Remove events outside the energy filtering window and retain events inside the energy filtering window as the set of valid events.

[0064] In this embodiment of the invention, the total energy distribution of an event is obtained by summing the energy values ​​of each channel in the event. A total energy spectrum is constructed using a histogram, and the position of the main photoelectric peak in the total energy spectrum is extracted using a peak detection method and recorded as the peak value, serving as a reference benchmark for subsequent screening and correction. The total energy of all events is then compared with the photoelectric peak position (peak), and an energy screening window is set to [0.8peak, 1.2peak] to eliminate noisy events. Only events within this energy window range are retained, resulting in a set of effective events with a high signal-to-noise ratio, which is used for model calculations in the subsequent correction process.

[0065] S4. Use an iterative algorithm to correct the channel gain coefficients. Initialize the channel gain coefficients. Each iteration includes: calculating the total energy of effective events for each channel according to the current gain coefficient; updating and correcting the gain coefficients of each channel based on the difference between the calculated total energy and the preset target energy; and monitoring the energy resolution in real time until convergence.

[0066] The iterative algorithm constructs an objective function based on the principle of minimum error. By comparing the difference between the total energy calculated for each event in the current iteration and the preset target energy, it allocates residuals by channel and back-calculates the correction amount of the gain coefficient of each channel to update the current gain coefficient until the residuals converge or the number of iterations reaches the preset upper limit.

[0067] Since this embodiment uses simulation results to obtain data with uniform channel gain, a perturbation simulation is performed on the original QDC data by introducing random gain coefficients. Specifically, a random gain coefficient following a Gaussian distribution in the interval of 0.5 to 2 is generated for each channel to form a channel-level gain coefficient vector. This gain coefficient is then multiplied item by item with the original QDC data to obtain a QDC array matrix with inconsistent simulated gain.

[0068] For the selected valid event data, a dynamic target energy value is set:

[0069]

[0070] The energy window width is gradually narrowed to ±2% of the photoelectric peak value to stabilize the proportion of effective events and accelerate subsequent convergence. Next, the MLEM iterative algorithm is used to obtain the gain coefficient, with the number of iterations set to N (2000 in the experiment). The channel gain estimate f is initialized. k =1 ( k(From 1 to 256). Update the gain coefficients in each iteration and calculate the current predicted total energy. Then, update the channel gain coefficient f according to the iterative formula of the MLEM algorithm. k value:

[0071]

[0072] Where, p i The target energy value is dynamically adjusted. The current gain coefficient is used as the energy prediction value, and the deviation between the predicted total energy and the target energy value is used as feedback. For the first i The first event k The charge response of the channel is expressed as an energy value, f. k (n+1) f is the gain coefficient for the iteration. k (n) is the current gain coefficient, and N is the total number of events. The energy represented by the position of the photoelectric peak in the total energy spectrum. α represents the actual total energy value of all channels for the current event, α is the compression factor, and K is the total number of channels.

[0073] The predicted energy value of the current gain coefficient is the sum of the energy values ​​of the current event recorded on each channel and the corresponding current gain coefficients. This yields the estimated total energy of the event at the current gain coefficient. This ratio measures the difference between the current estimate and the expectation, and is used to modulate the scaling factor to control the channel gain coefficient f. k .

[0074] The perturbation data is corrected using the current gain coefficient, and the energy resolution of the corrected data is calculated in real time. During the iteration process, the curve of energy resolution changing with the number of iterations is recorded for subsequent evaluation and dynamic convergence determination.

[0075] Specifically, real-time monitoring of energy resolution until convergence includes: stopping iteration when the change in energy resolution over N consecutive iterations is less than a threshold δ (δ < 0.1%); or stopping iteration when the number of iterations reaches a preset number of iterations M. The calculation of the energy resolution includes: performing Gaussian fitting on the total energy spectrum according to the formula... Calculate the energy resolution, where σ is the standard deviation of the Gaussian fit of the total energy spectrum curve.

[0076] S5. Apply the final obtained gain coefficient to perform channel-level gain compensation on all original event data, and output and display the corrected energy spectrum and gain distribution map.

[0077] In this embodiment of the invention, the method further includes evaluating the gain correction effect:

[0078] Generate theoretical gain distribution map and corrected gain distribution map, calculate the Pearson correlation coefficient between theoretical gain and corrected gain, and plot the curve of energy resolution change with the number of iterations to determine the relative gain difference between the detector channels.

[0079] A visual evaluation is performed based on the obtained gain coefficient correction results. Total energy spectrum histograms are plotted for the original, perturbed, and corrected data to visually reflect the photoelectric peak sharpening effect. The recovered gain coefficient vector f is then used to evaluate the results. k The image is restored to a 16×16 channel structure and compared with the theoretical gain distribution. A scatter plot is drawn between the restored gain and the theoretical gain, and the Pearson correlation coefficient is calculated. Finally, the resolution change curve during the iteration process is drawn, and the levels before, after, and after the perturbation are marked with dashed lines.

[0080] Figure 4 The energy spectrum is obtained by summing and statistically analyzing the initial energy data across all channels. The 140 keV photoelectric peak exhibits a typical Gaussian distribution. After Gaussian fitting, the full width at half maximum (FWHM) is 24.43 keV, corresponding to an energy resolution of 17.45%. This value reflects the intrinsic performance of the detector under ideal gain uniformity conditions, providing a benchmark for subsequent gain perturbation simulation and correction effect evaluation. Figure 5 The image shows the energy spectrum after perturbation. The photoelectric peak is significantly broadened, the FWHM increases to 27.16 keV, and the energy resolution degrades to 19.40%. This result clearly reflects the significant negative impact of channel gain inconsistency on the detector's energy resolution, verifying the necessity of gain correction. Figure 6 The energy spectrum is obtained by summing and statistically analyzing the energy data after MLEM algorithm iteration and the full-channel signal. The energy spectrum broadening caused by gain perturbation is significantly narrowed after MLEM algorithm processing.

[0081] Figure 7 This graph illustrates the change in energy resolution with the number of iterations (2000) provided in this embodiment of the invention. It shows a two-stage convergence rate. During the rapid convergence stage, the energy resolution drops rapidly from an initial 19.40% to 17.23%, as the algorithm quickly corrects the average gain difference of each channel by maximizing the likelihood function. Subsequently, it gradually converges, finally reaching 16.16% at 2000 iterations. The corrected energy resolution is improved by 13.1%, verifying the algorithm's effective suppression of complex gain non-uniformity.

[0082] Figure 8Figure (a) and (b) are the gain distribution diagrams before and after correction provided in the embodiments of the present invention. Figure (a) shows that the theoretical gain distribution exhibits random and non-uniform characteristics, with the maximum difference between channels reaching 3 times (minimum value 0.5, maximum value 2.0). Figure (b) shows the recovered gain distribution after MLEM algorithm iteration, with the gain difference between channels decreasing.

[0083] Figure 9 This is a gain correlation analysis chart. The scatter plots of the recovered gain and the theoretical gain are closely distributed around the y=x trend line. The Pearson correlation coefficient is 0.8586, indicating that the two have a relatively strong linear correlation.

[0084] This invention proposes a dynamic gain correction method based on an iterative gain coefficient algorithm that requires no additional hardware modification. By statistically modeling and iteratively optimizing event data collected by existing detectors, the gain coefficient of each channel is automatically estimated, and global energy resolution is used as the optimization index to achieve post-processing correction of the original data.

[0085] First, the raw energy values ​​of charge deposition in each channel of the multi-channel detector are obtained. The energy deposition value of each event in each channel is extracted, and the energy values ​​are statistically analyzed to construct the total energy spectrum of the event. The position of the photoelectric peak is located by statistical analysis, and an energy filtering window is set accordingly to filter out the effective subset of events near the energy peak from all events, eliminating interference from low-energy or high-energy noise events.

[0086] Furthermore, the event energy is adjusted by focusing the energy value towards the photoelectric peak to reduce the impact of noise from other energies on iterative convergence and improve iterative stability. Subsequently, the channel gain is estimated stepwise using the formula for iteratively updating the gain coefficient. A preset number of iterations is set, and the channel gain coefficient is updated in each iteration. The energy resolution of the perturbation data after current gain correction is calculated in real time, forming a resolution change trend curve to assist the algorithm in dynamic judgment.

[0087] Furthermore, the system can also perform gain evaluation and visualization, compare and analyze the total energy spectrum under three states: before correction, after disturbance, and after correction. At the same time, it restores the channel gain coefficient after correction to a two-dimensional structure heat map, and determines that the two have a linear correlation by using the preset scatter plot of the corrected gain and the Pearson correlation coefficient, thereby evaluating the accuracy of gain correction. It can also perform quantitative evaluation parameter settings to achieve system status monitoring.

[0088] Accordingly, embodiments of the present invention also provide a channel gain correction system for a multi-channel detector, such as... Figure 10 As shown, the system includes:

[0089] The data acquisition module 201 is used to acquire raw event data using a multi-channel detector. The event is a gamma photon event, and the raw event data includes the charge value and duration of each channel in the event.

[0090] The preprocessing module 202 is used to preprocess the collected raw event data, remove noise interference, and obtain the energy of all channels triggered by the same event by judging the action time.

[0091] The filtering module 203 is used to calculate the total energy value of each event through the energy of each channel, count all detected events, construct the total energy spectrum of the channel events, and set an energy filtering window according to the position of the photoelectric peak in the total energy spectrum, remove events outside the energy filtering window, and retain events inside the energy filtering window as a set of valid events.

[0092] The calibration module 204 is used to perform channel gain coefficient calibration using an iterative algorithm, initialize the channel gain coefficient, and each iteration includes: calculating the total energy of effective events for each channel according to the current gain coefficient, updating and calibrating the gain coefficient of each channel according to the difference between the calculated total energy and the preset target energy, and monitoring the energy resolution in real time until convergence;

[0093] The output and visualization module 205 is used to apply the final obtained gain coefficient to perform channel-level gain compensation on all raw event data, and output and display the corrected energy spectrum and gain distribution map.

[0094] For ease of explanation, Figure 10 Only the main components of the system are shown. The system of this embodiment can be used to perform... Figure 1 The technical solutions of the method embodiments shown are similar in principle and in effect, and will not be described again here.

[0095] In summary, this invention provides a gain correction method and system for a multi-channel readout detector, which can adapt to data characteristics under high channel number and high noise backgrounds, effectively restore the consistency of response between channels, and improve the energy resolution and overall performance of the detector through algorithm optimization, providing new ideas for technological breakthroughs in high-end medical equipment.

[0096] In an exemplary embodiment, the present invention also provides an electronic device, the electronic device comprising:

[0097] processor;

[0098] A memory storing computer-readable instructions, which, when loaded and executed by the processor, implement the steps of the channel gain correction method for the multi-channel detector described above.

[0099] In an exemplary embodiment, the present invention also provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the steps of the channel gain correction method for a multi-channel detector as described above. For example, the computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, or optical data storage device, etc.

[0100] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0101] The use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0102] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0103] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0104] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0105] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0106] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0107] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0108] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0109] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for channel gain correction of a multi-channel detector, characterized in that, Includes the following steps: S1. Use a multi-channel detector to collect raw event data, wherein the event is a gamma photon event, and the raw event data includes the charge value and duration of each channel in the event; S2. Determine the energy of all channels triggered by the same event by judging the action time; S3. Calculate the total energy value for each event through the energy of each channel, count all detected events, construct the total energy spectrum of the channel events, and set an energy filtering window according to the position of the photoelectric peak in the total energy spectrum. Remove events outside the energy filtering window and retain events inside the energy filtering window as the set of valid events. S4. Use an iterative algorithm to correct the channel gain coefficients. Initialize the channel gain coefficients. Each iteration includes: calculating the total energy of effective events for each channel according to the current gain coefficient; updating and correcting the gain coefficients of each channel based on the difference between the calculated total energy and the preset target energy; and monitoring the energy resolution in real time until convergence. S5. Apply the final obtained gain coefficient to perform channel-level gain compensation on all original event data, and output and display the corrected energy spectrum and gain distribution map.

2. The channel gain correction method for a multi-channel detector according to claim 1, characterized in that, The iterative algorithm constructs an objective function based on the principle of minimum error. By comparing the difference between the total energy calculated for each event in the current iteration and the preset target energy, it allocates residuals by channel and back-calculates the correction amount of the gain coefficient of each channel to update the current gain coefficient until the residuals converge or the number of iterations reaches the preset upper limit.

3. The channel gain correction method for a multi-channel detector according to claim 1, characterized in that, In the iterative algorithm, the first... k The gain coefficient of each channel is calculated as follows: Where, p i The target energy value is dynamically adjusted. The current gain coefficient is used as the energy prediction value, and the deviation between the predicted total energy and the target energy value is used as feedback. For the first i The first event k The charge response of the channel is expressed as an energy value, f. k (n+1) f is the gain coefficient for the iteration. k (n) is the current gain coefficient, and N is the total number of events.

4. The channel gain correction method for a multi-channel detector according to claim 3, characterized in that, The target energy value is calculated as follows: in, The energy represented by the position of the photoelectric peak in the total energy spectrum. The actual total energy value of all channels for the current event, where α is the compression factor; and the energy prediction value of the current gain factor. The calculation method is as follows: Where K is the total number of channels.

5. The channel gain correction method for a multi-channel detector according to claim 1, characterized in that, The real-time monitoring of energy resolution until convergence specifically includes: The iteration stops when the energy resolution change is less than the threshold δ after N consecutive iterations, where δ < 0.1%; or when the number of iterations reaches the preset number of iterations M.

6. The channel gain correction method for a multi-channel detector according to claim 1, characterized in that, The method also includes evaluating the gain correction effect: Generate theoretical gain distribution map and corrected gain distribution map, calculate the Pearson correlation coefficient between theoretical gain and corrected gain, and plot the curve of energy resolution change with the number of iterations to determine the relative gain difference between the detector channels.

7. The channel gain correction method for a multi-channel detector according to claim 1, characterized in that, The calculation of the energy resolution includes: Gaussian fitting was performed on the total energy spectrum according to the formula. Calculate the energy resolution, where σ is the standard deviation of the Gaussian fit of the total energy spectrum curve.

8. A channel gain correction system for a multi-channel detector, said system being used to implement the method as described in any one of claims 1 to 7, characterized in that, The system includes: The data acquisition module is used to acquire raw event data using a multi-channel detector. The event is a gamma photon event, and the raw event data includes the charge value and duration of each channel in the event. The preprocessing module is used to preprocess the collected raw event data, remove noise interference, and obtain the energy of all channels triggered by the same event by determining the action time. The filtering module is used to calculate the total energy value of each event through the energy of its respective channels, count all detected events, construct the total energy spectrum of the channel events, and set an energy filtering window according to the position of the photoelectric peak in the total energy spectrum to remove events outside the energy filtering window and retain events inside the energy filtering window as the set of valid events. The calibration module is used to perform channel gain coefficient calibration using an iterative algorithm. It initializes the channel gain coefficients, and each iteration includes: calculating the total energy of effective events for each channel based on the current gain coefficient, updating and calibrating the gain coefficients of each channel based on the difference between the calculated total energy and the preset target energy, and monitoring the energy resolution in real time until convergence. The output and visualization module is used to apply the final obtained gain coefficients to perform channel-level gain compensation on all raw event data, and output and display the corrected energy spectrum and gain distribution map.

9. An electronic device, characterized in that, The electronic device includes: processor; A memory storing computer-readable instructions that, when loaded and executed by the processor, implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 7.

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