A detector calibration method and related device

Abstract

The present application discloses a detector calibration method and related devices, which relate to the field of radiological imaging. Based on the dot matrix marking method, the output voltage signal of the photomultiplier tube for each gamma event of the radioactive source at each test position on the detector surface is obtained, and the output voltage signal set of each photomultiplier tube is obtained. All output voltage signal sets are statistically processed to obtain statistical characteristic data of the output voltage. The statistical characteristic data are fitted to obtain the relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons. Based on a preset search algorithm, the maximum value of the likelihood function that characterizes the relationship between the number of photoelectrons of each photomultiplier tube and each test position is solved to obtain the position with the largest number of photoelectrons on the detector surface, wherein the likelihood function is constructed by the average response function determined by the relationship function and the number of photoelectrons of each photomultiplier tube. It can better reflect the depth information of the energy deposited in the crystal, thereby improving the imaging quality of the detector.

Description

Technical Field

[0001] The present application relates to the field of radiological imaging technology, and in particular to a detector calibration method and related devices. Background Art

[0002] SPECT (Single-Photon Emission Computed Tomography) detectors are typically scintillator-based versions of gamma cameras. Each event generates an output on a PMT (photomultiplier tube) array. A simple centroid algorithm is often used to process the output signals from multiple PMTs in the array to estimate the two-dimensional spatial position and energy deposited by the event. However, due to issues with the centroid algorithm, such as low spatial resolution and edge distortion, image acquisition using detectors calibrated using this method results in poor image quality that cannot meet higher quality requirements. Summary of the Invention

[0003] In view of the above problems, this application provides a detector calibration method and related devices to achieve the purpose of improving imaging quality. The specific solution is as follows:

[0004] A first aspect of the present application provides a detector calibration method, comprising:

[0005] Based on the dot matrix marking method, the output voltage signal of the photomultiplier tube for each gamma event of the radioactive source at each test position on the detector surface is obtained to obtain the output voltage signal set of each photomultiplier tube;

[0006] Performing statistical processing on all output voltage signal sets to obtain statistical characteristic data of the output voltage;

[0007] Performing fitting processing on the statistical characteristic data to obtain a relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons;

[0008] Based on a preset search algorithm, the likelihood function characterizing the relationship between the number of photoelectrons in each photomultiplier tube and each test position is solved for its maximum value to obtain the position on the detector surface with the largest number of photoelectrons. The likelihood function is constructed by the average response function determined by the relationship function and the number of photoelectrons in each photomultiplier tube.

[0009] In a possible implementation, performing statistical processing on all output voltage signal sets to obtain statistical characteristic data of the output voltage includes:

[0010] Dividing the output voltage signals in the entire output voltage signal set according to the test positions to obtain a first output voltage divided set;

[0011] Dividing the first voltage division set according to each of the photomultiplier tubes to obtain a second output voltage division set;

[0012] Gaussian fitting is performed on the statistical histogram of the output voltage subset of each photomultiplier tube in the second output voltage partition set to obtain the mean and variance of the output voltage of each photomultiplier tube.

[0013] In a possible implementation, performing fitting processing on the statistical characteristic data to obtain a relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons includes:

[0014] Performing least squares polynomial fitting on the mean and variance of the output voltage of each photomultiplier tube at each test position to obtain an action coefficient characterizing the action relationship between the output voltage of the photomultiplier tube and the number of photoelectrons;

[0015] A relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons is obtained based on the action coefficient.

[0016] In a possible implementation, the process of determining the average response function includes:

[0017] Based on the relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons, the relationship function between the average output voltage of the photomultiplier tube and the average number of photoelectrons is obtained;

[0018] The average response function is obtained according to the relationship function between the average output voltage of the photomultiplier tube and the average number of photoelectrons.

[0019] In a possible implementation, the process of determining the number of photoelectrons of each photomultiplier tube includes:

[0020] The number of photoelectrons of the photomultiplier tube is calculated based on the action coefficient and a relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons.

[0021] In a possible implementation, the solving of the maximum value of the likelihood function between the number of photoelectrons representing each photomultiplier tube and each test position based on a preset search algorithm includes:

[0022] If the detector surface is a square, performing a preset number of iterations on the detector surface to obtain the maximum value of the likelihood function, and during the iterations, taking the position of the connecting line corresponding to the maximum value of the likelihood function value obtained in the previous iteration as the center of the first square area of the current iteration;

[0023] In the current iteration, each side of the first square area is divided equally into a preset number of first square sub-areas, where the side length of the first square sub-areas is half the side length of the second square sub-areas obtained in the previous iteration;

[0024] Calculate the likelihood function values at the intersection points of the connecting lines constituting the preset number of first square sub-areas, and determine the maximum value of this iteration therefrom; in the first iteration, divide the detector surface into the preset number of third square sub-areas after equally dividing each side of the detector surface; calculate the likelihood function values at the intersection points of the connecting lines constituting the preset number of third square sub-areas, and determine the maximum value of the first iteration therefrom.

[0025] In a possible implementation, it also includes: if the detector surface is non-square, after expanding the detector surface into a square area, performing a preset number of iterative processing on the expanded square area to obtain the maximum value of the likelihood function, and the likelihood function values of the area in the expanded square area except the detector surface are all 0.

[0026] A second aspect of the present application provides a detector calibration device, comprising:

[0027] An output signal acquisition module is used to obtain the output voltage signal of the photomultiplier tube for each gamma event of the radioactive source at each test position on the detector surface based on a dot matrix marking method, and obtain an output voltage signal set of each photomultiplier tube;

[0028] A characteristic data statistics module is used to perform statistical processing on all output voltage signal sets to obtain statistical characteristic data of the output voltage;

[0029] an action relationship determination module, configured to perform fitting processing on the statistical characteristic data to obtain a relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons; and

[0030] A likelihood function solving module is used to solve the maximum value of the likelihood function characterizing the relationship between the number of photoelectrons of each photomultiplier tube and each test position based on a preset search algorithm to obtain the position on the detector surface with the maximum number of photoelectrons. The likelihood function is constructed by the average response function determined by the relationship function and the number of photoelectrons of each photomultiplier tube.

[0031] A third aspect of the present application provides a computer program product comprising computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements the detector calibration method of the first aspect or any implementation of the first aspect.

[0032] A fourth aspect of the present application provides an electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0033] The memory is used to store computer programs;

[0034] The processor is used to execute the computer program so that the electronic device can implement the detector calibration method of the above-mentioned first aspect or any implementation manner of the first aspect.

[0035] In a fifth aspect, the present application provides a computer storage medium, which carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can use the detector calibration method of the above-mentioned first aspect or any implementation of the first aspect.

[0036] By means of the above-mentioned technical solution, the detector calibration method provided in this application is based on a dot matrix marking method. The output voltage signal of the photomultiplier tube for each gamma event of the radioactive source at each test position on the detector surface is obtained, and the output voltage signal set of each photomultiplier tube is obtained. All output voltage signal sets are statistically processed to obtain statistical characteristic data of the output voltage. The statistical characteristic data is fitted to obtain a relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons. Based on a preset search algorithm, the likelihood function representing the relationship between the number of photoelectrons of each photomultiplier tube and each test position is solved for the maximum value, and the position with the maximum number of photoelectrons on the detector surface is obtained, where the likelihood function is constructed from the average response function determined by the relationship function and the number of photoelectrons of each photomultiplier tube. Compared with the traditional center of gravity method, the position information estimated by the dot matrix marking method and the likelihood function can better reflect the depth information of the energy deposited in the crystal, thereby improving the imaging quality of the detector. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the originals and elements are not necessarily drawn to scale.

[0038] Figure 1 A flow chart of a detector calibration method provided in this application;

[0039] Figure 2 An array arrangement diagram of a photomultiplier tube provided in this application;

[0040] Figure 3 The pan-field diagram of the lattice model provided for this application;

[0041] Figure 4 This is the result of automatic segmentation of the flood field image provided by this application;

[0042] Figure 5 Average response graph of the photomultiplier tubes provided for this application;

[0043] Figure 6 Example diagram of the maximum likelihood problem provided for this application;

[0044] Figure 7 A schematic diagram of the shrinking grid search algorithm provided for this application;

[0045] Figure 8 A schematic diagram of the directional search algorithm provided in this application;

[0046] Figure 9 Comparison chart of the effects of the center of gravity method and maximum likelihood algorithm provided for this application

[0047] Figure 10 A structural diagram of the detector calibration device provided for this application;

[0048] Figure 11 This is a structural diagram of the electronic device provided in this application. DETAILED DESCRIPTION

[0049] The following describes the embodiments of the present application in conjunction with the accompanying drawings. The terms used in the implementation methods of the present application are only used to explain the specific embodiments of the present application and are not intended to limit the present application.

[0050] The embodiments of the present application are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0051] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, and this is merely a way of distinguishing the objects of the same attributes when describing them in the embodiments of the present application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0052] The present invention provides a detector calibration method. The detector calibration method of the present invention is described in detail below with reference to the accompanying drawings.

[0053] Reference Figure 1 , Figure 1 A flow chart of a detector calibration method provided in an embodiment of the present application is shown as follows: Figure 1 As shown, the detector calibration method provided in the embodiment of the present application may include steps 101 to 104, and these steps are described in detail below.

[0054] 101. Based on the dot matrix marking method, the output voltage signal of the photomultiplier tube for each gamma event of the radiation source at each test position on the detector surface is obtained to obtain the output voltage signal set of each photomultiplier tube.

[0055] Specifically, refer to Figure 2 The photomultiplier tube array of a commonly used detector is shown. The detector includes a whole piece of NaI(Tl) crystal (585×470×9.5mm 3 ) and 55 photomultiplier tubes (49 R6233 (Φ76mm) and 6 R6231 (Φ50mm)). The output signal of each PMT is amplified and digitized by electronic systems such as the preamplifier, VGA circuit (video graphics array), and ADC (analog-to-digital converter). It then passes through the FPGA and communication modules, and is ultimately collected by terminal software such as a computer to obtain a digitized voltage signal. When implementing detector calibration using a likelihood function, the detector average response function must be calibrated. In actual applications, the detector output voltage signal is obtained, so parameters such as the gain of each photomultiplier tube and the gain of the electronics processed by the subsequent circuits also require calibration.

[0056] The commonly used direct calibration method uses a collimated source to scan a two-dimensional grid of dots across the surface of a scintillator crystal, collecting a sufficient number of gamma events at each point. This data is then processed to obtain the average response of M photomultiplier tubes at that point. The average responses of all PMTs at all locations are then combined to produce the detector's average response function. While simple and intuitive, this method is labor-intensive and time-consuming, and requires specialized, precise mechanical movement equipment.

[0057] To overcome the shortcomings of direct calibration, this application uses a dot matrix calibration method. During the calibration process, a tungsten plate with evenly distributed circular holes is placed on the detector surface. The detector surface is illuminated with a flood field generated by a metastable technetium isomer, and sufficient gamma event data is collected to obtain a flood field image.

[0058] For example, a 3mm thick tungsten plate with a 55×43 array of 3mm circular holes is placed on the plate. The center spacing of the holes is 9.86mm horizontally and 9.95mm vertically. The pan-field image obtained by placing it on the detector surface is as follows: Figure 3 As shown in the figure, the flood field map is automatically segmented using the watershed algorithm in Matlab. The results are as follows: Figure 4 The overall segmentation results are quite satisfactory, requiring only manual correction of segmentation errors at the edges. Based on the segmentation results, the collected gamma event data are then divided into 55 × 43 groups, each corresponding to a hole on the tungsten plate. This means that the spatial locations of the gamma events within each group are consistent, located at the corresponding hole on the tungsten plate. Similar to the direct calibration method, calibration is completed by performing data processing. The 2365 data sets obtained by segmenting the data collected using the dot matrix calibration method are equivalent to the data collected by moving the pencil beam 2365 positions in the direct calibration method. Therefore, the dot matrix calibration method significantly reduces the workload and does not require precise and complex mechanical movement devices.

[0059] The data to be processed is the voltage signal output by all the photomultiplier tubes that respond when a gamma event occurs at different locations on the detector surface. For each test location R on the detector surface, the detector detects a large number of gamma events N. For each gamma event, some photomultiplier tubes in the detector will output a signal V m These data can be expressed as {V m (R, i)}, where m is the serial number of the photomultiplier tube, R is the test position, and i is the serial number of the gamma event (i = 1, 2, 3 . . . , N).

[0060] It is understandable that those skilled in the art may adjust the size of the tungsten plate and the circular hole in the dot matrix calibration method as needed, and no limitation is imposed here.

[0061] 102. Perform statistical processing on all output voltage signal sets to obtain statistical characteristic data of the output voltage.

[0062] Specifically, the output voltage signals in the entire output voltage signal set are divided according to the test position to obtain a first output voltage divided set:

[0063] According to the different incident positions R on the detector surface, the data are divided into multiple groups, and a series of {V m (i)}.

[0064] Divide the first voltage partition set according to each photomultiplier tube to obtain the second output voltage partition set:

[0065] {V m (i)} is divided into multiple groups {V (i)} according to the difference of m.

[0066] Gaussian fitting is performed on the statistical histogram of the output voltage subset of each photomultiplier tube in the second output voltage partition set to obtain the mean and variance of the output voltage of each photomultiplier tube:

[0067] Draw a statistical histogram of {V (i)} and use the peak in the Gaussian fit graph to get the mean And variance Var(V), perform the previous step on all group data and get { (R)} and {Var(V) m (R)}.

[0068] 103. Fit the statistical characteristic data to obtain the relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons.

[0069] Specifically, the mean and variance of the output voltage of each photomultiplier tube at each test position are fitted with a least squares polynomial to obtain an action coefficient representing the interaction between the photomultiplier tube output voltage and the number of photoelectrons. Based on this action coefficient, a relationship function between the photomultiplier tube output voltage and the number of photoelectrons is obtained.

[0070] After the mean and variance of the test positions obtained above are grouped according to m, multiple groups { (R)} and {V ar(V )(R)}, all R corresponding to ( , Var (V )) are plotted on a graph, and we can see that these points are linearly related. We can perform a least squares polynomial fit and obtain the formula for the fitted line.

[0071] According to the V of the electronically amplified signal output by the photomultiplier tube m formula:

[0072] (1)

[0073] Combining the above fitting straight line formula, the gain G of each photomultiplier tube can be obtained m and the electronics gain V m0 .

[0074] According to the relationship function between the average output voltage of the photomultiplier tube and the average number of photoelectrons, the average response function is obtained. Then, based on the action coefficient and the relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons, the number of photoelectrons of the photomultiplier tube is converted: V output by the photomultiplier tube m The mean of is:

[0075] (2)

[0076] The output signal V mThe number of photoelectrons n on the photocathode of the photomultiplier tube m Linked together, (R)} is converted to { (R)}, the average response function of the detector can be obtained.

[0077] The MDRF (mean response function) obtained through the calibration process is often displayed as a series of pictures. Each picture corresponds to a photomultiplier tube, and each pixel of the picture corresponds to a spatial position on the detector surface. The pixel value represents the average number of photoelectrons generated on the photocathode of the photomultiplier tube when a gamma event occurs at this position. Figure 5 shown.

[0078] 104. Based on a preset search algorithm, the likelihood function representing the relationship between the number of photoelectrons of each photomultiplier tube and each test position is solved for the maximum value to obtain the position on the detector surface with the largest number of photoelectrons. The likelihood function is constructed by the average response function determined by the relationship function and the number of photoelectrons of each photomultiplier tube.

[0079] Specifically, for the parameter estimation task of detector signal processing, the likelihood function is in the following form:

[0080] pr(g|θ) (3)

[0081] Here, θ is an N × 1 vector representing the N parameters to be estimated, such as spatial position (x, y, z), energy E, time t, etc. g is an M × 1 data vector representing the M collected detector signals, such as the output voltage signal of a photomultiplier tube.

[0082] The maximum likelihood estimate of the likelihood function is to choose a The value of maximizes the likelihood function. The formula for ML (maximum likelihood estimation) is as follows:

[0083] (4)

[0084] For the convenience of calculation, the equivalent form of the ML formula is selected in the actual application process as follows:

[0085] Given prior knowledge of θ, we can exploit this information by using Maximum a posteriori probability estimation (MAP). The MAP formula is as follows:

[0086] (5)

[0087] It can be seen that MAP only has an additional prior probability weight compared to ML. When the probability of all values of θ is the same, pr(θ) is a constant, and MAP degenerates into ML.

[0088] Based on the above maximum likelihood estimation theory, the construction process of the statistical model of the scintillator detector signal specifically includes:

[0089] A 140 keV gamma photon emitted by a metastable technetium isomer can interact with a NaI(Tl) crystal in two ways: the photoelectric effect and Compton scattering. The energy of photoelectrons, X-rays, and Auger electrons generated by the photoelectric effect is deposited within a 100µm scale, and the resulting conversion to scintillation photons is negligibly small for the detector's spatial resolution (∼ mm). However, during Compton scattering, the energy lost by the gamma photon is converted into scintillation light, but the scattered photon travels away from the site of interaction, either continuing to interact with the NaI(Tl) crystal or escaping it.

[0090] NaI(Tl) crystals can convert the energy deposited by γ photons into scintillation light. Assuming that the response of the scintillation crystal to the deposited energy is linear, the average number of scintillation photons is:

[0091] (6)

[0092] Among them, Q sc is the luminous efficiency of the scintillator, that is, the average number of scintillation photons emitted per unit of deposited energy. E is the energy value deposited by the gamma photons.

[0093] For scintillators, it is usually assumed that the specific number of scintillation photons obeys a Poisson distribution. However, in reality, many scintillators, especially NaI(Tl) crystals, have a nonlinear response to energy.

[0094] Since the average number of scintillation photons is also a random variable, the average number of scintillation photons follows a doubly random Poisson process.

[0095] Based on the fact that the average number of scintillation photons follows a Poisson distribution, both the visible light transport process and the photoelectric conversion process on the photocathode of a photomultiplier tube are binomial processes. According to the basic principle of statistics: the binomial choice of a Poisson random variable is still a Poisson random variable, it can be seen that the number of photoelectrons generated on the photocathode of each photomultiplier tube is also a Poisson random variable, and the average number of photoelectrons is:

[0096] (7)

[0097] Among them, vector R=(x,y,z) is the spatial coordinate where the γ photon acts, nm is the number of photoelectrons in the photocathode of the mth photomultiplier tube, η m represents the quantum efficiency of the photocathode, f m (R) represents the fraction of scintillation photons reaching the photocathode of the mth photomultiplier tube.

[0098] According to the Poisson distribution formula, n is observed in the case of R, E m The probability of a photoelectron is:

[0099] (8)

[0100] For n observed by different photomultiplier tubes m are independent of each other, so for M photomultiplier tubes, {n m , m =1, ..., M} photoelectrons are multivariate probabilities:

[0101] (9)

[0102] The above formula is a likelihood function that characterizes the relationship between the number of photoelectrons of each photomultiplier tube and each test position.

[0103] The nonlinearity of the scintillator energy response causes the number of scintillation photons to not obey the Poisson distribution. Since the number of scintillation photons is a relatively large value, the quantum efficiency and photon transport issues η m f m (R) is a relatively small fraction. The process of whether the scintillation photons can reach the photocathode of the photomultiplier tube and generate photoelectrons can be regarded as a binomial distribution process. According to the law in statistics: for a binomial distribution, if the probability is small and the number of trials is large, its probability distribution can be approximately considered as a Poisson distribution. Therefore, regardless of whether the number of scintillation photons obeys the Poisson distribution, n m The assumptions about Poisson distribution are all valid. Therefore, the likelihood function obtained above is valid.

[0104] Using the above formula (2) { (R)} is converted to { (R)}, the average response function of the detector can be obtained. According to formula (1), the voltage signal output by the photomultiplier tube is converted into the number of photoelectrons, and then it is substituted into formula (9) to solve the maximum likelihood problem.

[0105] For the current maximum likelihood problem, its characteristic is that there is only one maximum value on the rectangular domain, such as Figure 6 As shown. The shrinking grid search algorithm can be used to solve it. The specific process is as follows:

[0106] If the detector surface is a square, a preset number of iterative processes are performed on the detector surface to obtain the maximum value of the likelihood function. When performing iterative processing, the position of the connecting line corresponding to the maximum value of the likelihood function value obtained in the previous iteration is used as the center of the first square area of the current iteration.

[0107] In the current iteration, each side of the first square area is divided equally into a preset number of first square sub-areas, and the side length of the first square sub-area is half of the side length of the second square sub-area obtained in the previous iteration.

[0108] Calculate the likelihood function values at the intersection points of the connecting lines constituting a preset number of first square sub-areas, and determine the maximum value of this iteration therefrom. In the first iteration, divide each side of the detector surface into equal parts, then divide the detector surface into a preset number of third square sub-areas. Calculate the likelihood function values at the intersection points of the connecting lines constituting the preset number of third square sub-areas, and determine the maximum value of the first iteration therefrom.

[0109] If the detector surface is non-square, the detector surface is expanded into a square area, and the expanded square area is iteratively processed a preset number of times to obtain the maximum value of the likelihood function, where the likelihood function values of the area in the expanded square area except the detector surface are all 0.

[0110] For details, please refer to Figure 7 The following is an example of a shrinking grid search algorithm. For example, the search domain is a 78 × 78 dot matrix. To facilitate the execution of the algorithm, the 78 × 78 dot matrix of the detector is first expanded to 128 × 128, and the likelihood function value of the expanded point is fixed to 0. Then, the center of the detector surface is taken as the initial value of the center point of the first iteration, and the grid width is 32, as shown in the following figure. Figure 7 The likelihood function is calculated at these 16 points on the 4 × 4 grid in (a). The coordinates of the point with the maximum likelihood function value are taken as the center point of the next iteration, and the grid spacing of the next iteration is set to half the grid spacing of the current iteration. After a fixed number of iterations, the algorithm will search for the location of the maximum likelihood function value. For the case of the detector surface with a 78 × 78 grid, the number of iterations of the shrinking grid search algorithm is 6.

[0111] In addition, you can also use Figure 8 The directional search algorithm shown in Figure 1 can be divided into multiple iterations. In the first iteration, the search algorithm calculates the likelihood function value on a 5×5 grid of equally spaced points, and selects the point with the largest likelihood function as (x0, y0). Figure 8As shown in (a) above, in the second iteration, the search algorithm calculates the likelihood function values for the eight points in the neighborhood of (x0, y0) and takes the coordinates of the point with the maximum likelihood function as (x1, y1). This second iteration is repeated until the algorithm converges to the maximum likelihood function value (xk, yk).

[0112] For directed search algorithms, the speed of the algorithm is closely linked to the selection of the initial point (x0, y0). If the initial point is very close to the final extreme point, the algorithm iteration count is minimal. Directed search algorithms use a 5 × 5 grid to determine the initial point. The accuracy of this method is related to the number of grid points. A larger number of grid points results in a closer proximity to the extreme point, but this also requires calculating probability values for more points. Using the results of the Anger algorithm as the initial point for the directed search algorithm is a good choice, as it not only ensures a close proximity to the extreme point but also increases computational speed.

[0113] Alternatively, the most direct and simple exhaustive method can be used to calculate the likelihood function values at all positions and take the maximum value to obtain θ. This method produces accurate results but is the slowest.

[0114] Compared to exhaustive search, the directed search algorithm is significantly faster and produces more accurate results. The number of iterations required to reach convergence varies, and increases significantly as the search domain expands. The combination of the Anger algorithm and the directed search algorithm is an optimization of the directed search algorithm.

[0115] The shrinking grid search algorithm is fast and requires a fixed number of iterations for convergence, making it simpler and easier to execute on various platforms. Compared to other algorithms, the shrinking grid search algorithm is more suitable for larger search domains. While the computational complexity of other search algorithms increases significantly with a sharp increase in the number of points in the search domain, the shrinking grid search algorithm only requires one or two additional iterations (i.e., dozens of additional point likelihood function calculations), resulting in a much slower increase in computational complexity than other algorithms.

[0116] This detector calibration method uses the maximum likelihood estimation algorithm to estimate parameters such as spatial position with excellent performance and no distortion. It is combined with the dot matrix annotation method to calibrate the detector, which can retain more depth information of the detector, thereby making the obtained detector image quality higher.

[0117] And refer to Figure 9 As shown in the figure, the left side is the projection image of the detector surface determined using the center of gravity method, and the right side is the projection image of the detector surface estimated according to the likelihood function of this application. It can be seen that compared with the traditional center of gravity method, the gamma photon position determined by this application is clearer and has less edge distortion.

[0118] A detector calibration method provided in an embodiment of the present application is described above. The following describes an apparatus for executing the above detector calibration method.

[0119] See also Figure 10 , Figure 10 This is a schematic diagram of the structure of a detector calibration device provided in an embodiment of the present application. Figure 9 As shown, the detector calibration device includes:

[0120] The output signal acquisition module 1001 is used to obtain the output voltage signal of the photomultiplier tube for each gamma event of the radioactive source at each test position on the detector surface based on the dot matrix marking method, and obtain the output voltage signal set of each photomultiplier tube;

[0121] The characteristic data statistics module 1002 is used to perform statistical processing on all output voltage signal sets to obtain statistical characteristic data of the output voltage;

[0122] The function relationship determination module 1003 is used to perform fitting processing on the statistical characteristic data to obtain a relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons; and

[0123] The likelihood function solving module 1004 is used to solve the maximum value of the likelihood function that characterizes the relationship between the number of photoelectrons of each photomultiplier tube and each test position based on a preset search algorithm to obtain the position on the detector surface with the maximum number of photoelectrons. The likelihood function is constructed by the average response function determined by the relationship function and the number of photoelectrons of each photomultiplier tube.

[0124] In one possible implementation, the characteristic data statistics module 1002 performs statistical processing on all output voltage signal sets to obtain statistical characteristic data of the output voltage, including:

[0125] Dividing the output voltage signals in the entire output voltage signal set according to the test positions to obtain a first output voltage divided set;

[0126] Dividing the first voltage division set according to each of the photomultiplier tubes to obtain a second output voltage division set;

[0127] Gaussian fitting is performed on the statistical histogram of the output voltage subset of each photomultiplier tube in the second output voltage partition set to obtain the mean and variance of the output voltage of each photomultiplier tube.

[0128] In a possible implementation, the process of the action relationship determination module 1003 performing fitting processing on the statistical characteristic data to obtain a relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons includes:

[0129] Performing least squares polynomial fitting on the mean and variance of the output voltage of each photomultiplier tube at each test position to obtain an action coefficient characterizing the action relationship between the output voltage of the photomultiplier tube and the number of photoelectrons;

[0130] A relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons is obtained based on the action coefficient.

[0131] In one possible implementation, the process of determining the average response function in the likelihood function solving module 1004 includes:

[0132] Based on the relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons, the relationship function between the average output voltage of the photomultiplier tube and the average number of photoelectrons is obtained;

[0133] The average response function is obtained according to the relationship function between the average output voltage of the photomultiplier tube and the average number of photoelectrons.

[0134] In a possible implementation, the process of determining the number of photoelectrons of each photomultiplier tube in the likelihood function solving module 1004 includes:

[0135] The number of photoelectrons of the photomultiplier tube is calculated based on the action coefficient and a relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons.

[0136] In one possible implementation, the likelihood function solving module 1004 solves the likelihood function representing the number of photoelectrons in each photomultiplier tube and each test position for a maximum value based on a preset search algorithm, including:

[0137] If the detector surface is a square, performing a preset number of iterations on the detector surface to obtain the maximum value of the likelihood function, and during the iterations, taking the position of the connecting line corresponding to the maximum value of the likelihood function value obtained in the previous iteration as the center of the first square area of the current iteration;

[0138] In the current iteration, each side of the first square area is divided equally into a preset number of first square sub-areas, where the side length of the first square sub-areas is half the side length of the second square sub-areas obtained in the previous iteration;

[0139] Calculate the likelihood function values at the intersection points of the connecting lines constituting the preset number of first square sub-areas, and determine the maximum value of this iteration therefrom; in the first iteration, divide the detector surface into the preset number of third square sub-areas after equally dividing each side of the detector surface; calculate the likelihood function values at the intersection points of the connecting lines constituting the preset number of third square sub-areas, and determine the maximum value of the first iteration therefrom.

[0140] In one possible implementation, the likelihood function solving module 1004 is also used to, if the detector surface is non-square, expand the detector surface into a square area, and then perform a preset number of iterative processing on the expanded square area to obtain the maximum value of the likelihood function, and the likelihood function values of the areas in the expanded square area except the detector surface are all 0.

[0141] An electronic device is also provided in an embodiment of the present application. Figure 11 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic device in the embodiments of the present application may include, but is not limited to, fixed terminals such as mobile phones, laptops, PDAs (personal digital assistants), PADs (tablet computers), desktop computers, etc. Figure 11 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0142] like Figure 11 As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 1101, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1102 or programs loaded from a storage device 1108 into a random access memory (RAM) 1103. When the electronic device is powered on, the RAM 1103 also stores various programs and data required for the operation of the electronic device. The processing device 1101, ROM 1102, and RAM 1103 are interconnected via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0143] Typically, the following devices may be connected to the I / O interface 1105: an input device 1106 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 1107 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1108 including, for example, a memory card, a hard disk, etc.; and a communication device 1109. The communication device 1109 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Figure 11The electronic device is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0144] An embodiment of the present application also provides a computer program product including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements any detector calibration method provided in the embodiment of the present application.

[0145] A computer-readable storage medium is also provided in an embodiment of the present application. The storage medium carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement any detector calibration method provided in the embodiment of the present application.

[0146] It should also be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided in this application, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines.

[0147] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course can also be implemented by special hardware including application-specific integrated circuits, special CPUs, special memories, special components, etc. In general, all functions performed by computer programs can be easily implemented with corresponding hardware, and the specific hardware structures used to implement the same function can also be diverse, such as analog circuits, digital circuits or special circuits, etc. However, for the present application, software program implementation is a better implementation method in most cases. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer's floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk or optical disk, etc., and includes a number of instructions to enable a computer device (which can be a personal computer, training equipment, or network equipment, etc.) to execute the methods described in each embodiment of the present application.

[0148] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0149] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, a computer, a training device or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website, a computer, a training device or a data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a training device, a data center, etc. that includes one or more available media integrations. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

Claims

1. A detector calibration method, characterized in that: include: Based on the dot matrix marking method, the output voltage signal of the photomultiplier tube for each gamma event of the radioactive source at each test position on the detector surface is obtained to obtain the output voltage signal set of each photomultiplier tube; Performing statistical processing on all output voltage signal sets to obtain statistical characteristic data of the output voltage; Performing least squares polynomial fitting processing on the statistical characteristic data to obtain a relationship function between the gain of each photomultiplier tube, the gain of electronics, and the output voltage of the photomultiplier tube and the number of photoelectrons; Solving the maximum value of a likelihood function representing the relationship between the number of photoelectrons of each photomultiplier tube and each test position based on a preset search algorithm to obtain the position on the detector surface with the maximum number of photoelectrons, the likelihood function being constructed from the average response function determined by the relationship function and the number of photoelectrons of each photomultiplier tube; The process of determining the average response function includes: Obtaining an average output voltage of the photomultiplier tubes based on the gain of each photomultiplier tube and the gain of the electronics; Based on the relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons, obtaining the relationship function between the average output voltage of the photomultiplier tube and the average number of photoelectrons; The average response function is obtained according to the relationship function between the average output voltage of the photomultiplier tube and the average number of photoelectrons.

2. The detector calibration method according to claim 1, characterized in that: The statistical processing of all output voltage signal sets to obtain statistical characteristic data of the output voltage includes: Dividing the output voltage signals in the entire output voltage signal set according to the test positions to obtain a first output voltage divided set; Dividing the first output voltage divided set according to each of the photomultiplier tubes to obtain a second output voltage divided set; Gaussian fitting is performed on the statistical histogram of the output voltage subset of each photomultiplier tube in the second output voltage partition set to obtain the mean and variance of the output voltage of each photomultiplier tube.

3. The detector calibration method according to claim 2, characterized in that: The statistical characteristic data is subjected to least squares polynomial fitting processing to obtain the relationship function between the gain of each photomultiplier tube, the gain of electronics, the output voltage of the photomultiplier tube and the number of photoelectrons, including: Performing a least squares polynomial fitting on the mean and variance of the output voltage of each photomultiplier tube at each test position to obtain the gain of each photomultiplier tube, the electronic gain, and the action coefficient representing the relationship between the output voltage of the photomultiplier tube and the number of photoelectrons; A relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons is obtained based on the action coefficient.

4. The detector calibration method according to claim 3, characterized in that: The process of determining the number of photoelectrons of each photomultiplier tube comprises: The number of photoelectrons of the photomultiplier tube is calculated based on the action coefficient and a relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons.

5. The detector calibration method according to claim 1, characterized in that: The method of finding the maximum value of the likelihood function representing the relationship between the number of photoelectrons in each photomultiplier tube and each test position based on a preset search algorithm includes: If the detector surface is a square, performing a preset number of iterations on the detector surface to obtain the maximum value of the likelihood function, and during the iterations, taking the position of the connecting line corresponding to the maximum value of the likelihood function value obtained in the previous iteration as the center of the first square area of the current iteration; In the current iteration, each side of the first square area is divided equally into a preset number of first square sub-areas, where the side length of the first square sub-areas is half the side length of the second square sub-areas obtained in the previous iteration; Calculate the likelihood function values at the intersection points of the connecting lines constituting the preset number of first square sub-areas, and determine the maximum value of this iteration therefrom; in the first iteration, divide the detector surface into the preset number of third square sub-areas after equally dividing each side of the detector surface; calculate the likelihood function values at the intersection points of the connecting lines constituting the preset number of third square sub-areas, and determine the maximum value of the first iteration therefrom.

6. The detector calibration method according to claim 5, characterized in that: Also includes: If the detector surface is non-square, the detector surface is expanded into a square area, and the expanded square area is iteratively processed a preset number of times to obtain the maximum value of the likelihood function, and the likelihood function values of the area in the expanded square area except the detector surface are all 0.

7. A detector calibration device, characterized in that: include: An output signal acquisition module is used to obtain the output voltage signal of the photomultiplier tube for each gamma event of the radioactive source at each test position on the detector surface based on a dot matrix marking method, and obtain an output voltage signal set of each photomultiplier tube; A characteristic data statistics module is used to perform statistical processing on all output voltage signal sets to obtain statistical characteristic data of the output voltage; an action relationship determination module, configured to perform least squares polynomial fitting processing on the statistical characteristic data to obtain a relationship function between the gain of each photomultiplier tube, the gain of electronics, and the output voltage of the photomultiplier tube and the number of photoelectrons; as well as, a likelihood function solving module, configured to solve, based on a preset search algorithm, a likelihood function representing the relationship between the number of photoelectrons in each photomultiplier tube and each test position to obtain a maximum value, thereby obtaining a position on the detector surface at which the number of photoelectrons is maximum, wherein the likelihood function is constructed from an average response function determined by the relationship function and the number of photoelectrons in each photomultiplier tube; The process of determining the average response function in the likelihood function solution module includes: Obtaining an average output voltage of the photomultiplier tubes based on the gain of each photomultiplier tube and the gain of the electronics; Based on the relationship function between the output voltage of the photomultiplier tube and the number of photoelectrons, obtaining the relationship function between the average output voltage of the photomultiplier tube and the average number of photoelectrons; The average response function is obtained according to the relationship function between the average output voltage of the photomultiplier tube and the average number of photoelectrons.

8. An electronic device, characterized in that: comprising at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is configured to execute the computer program so that the electronic device can implement the detector calibration method according to any one of claims 1 to 6.

9. A computer storage medium, characterized in that The storage medium carries one or more computer programs, and when the one or more computer programs are executed by an electronic device, the electronic device can implement the detector calibration method according to any one of claims 1 to 6.

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