A gamma detection system, method and handheld gamma detection device

Abstract

The present disclosure provides a gamma detection system, including a detection module for receiving incident gamma photons and obtaining the position information, energy information, and count information M of the gamma photons; a processing module for statistically analyzing the gamma photons within a certain period of time to obtain their projection distribution data p, determining the energy E of the incident photons according to the energy spectrum statistically obtained, and thereby reading in the pre-stored left and right singular matrices V, UT, and diagonal matrix S, and determining the number N of singular values to be retained according to the count information M; a reconstruction module for processing the diagonal matrix S according to the number N of singular values to be retained, calculating the generalized inverse matrix C+ of the transfer matrix C of the detection module according to C+ = VS+UT, and calculating the reconstructed image according to f = C+p; and a display module for displaying the reconstructed image f, the energy spectrum of the incident gamma photons, and the count information M. The present disclosure can quickly realize gamma radiation reconstruction imaging with a small amount of calculation and is convenient for integration with small hardware circuits.

Description

Technical Field

[0001] The present disclosure relates to the fields of nuclear technology, nuclear applications, and nuclear medicine technology, and in particular, to a gamma detection system, method, and handheld gamma detection device. Background Art

[0002] Nuclear radiation detection equipment is widely installed and used in various fields such as industry, nuclear power plants, hospitals, environmental protection, and transportation hubs. Among them, for use as security inspection in crowded places, fixed channel-type equipment is generally selected, such as portal detectors, and combined with handheld radiation dose meters to conduct a carpet search on people or objects again. These devices often give alarms quickly, but due to the inability to visually locate, it is very difficult to quickly give the accurate distribution of radioactive substances, resulting in difficulties in finding and accurately positioning radiation sources. Considering that security personnel and others have used handheld devices such as metal detectors to conduct relevant inspections on personnel during routine security inspections, there is an increasingly strong demand for developing portable and highly integrated visual handheld nuclear radiation detection equipment in combination with existing monitoring processes to screen and solve problems such as radiation source leakage or loss. However, although existing nuclear radiation equipment based on coded plate imaging or Compton scattering principle can quickly lock the radiation source, its price is usually relatively expensive, and its volume and weight are relatively large, which is not conducive to popularization and use. Summary of the Invention

[0003] (1) Technical Problems to be Solved

[0004] The present disclosure provides a gamma detection system, method, and handheld gamma detection device to at least partially solve the above-mentioned technical problems.

[0005] (2) Technical Solutions

[0006] According to one aspect of the present disclosure, there is provided a gamma detection system, including:

[0007] A detection module, configured to receive incident gamma photons, and obtain the position information, energy information, and count information M of the gamma photons;

[0008] A processing module, which statistically processes the gamma photons within a certain period of time to obtain their projection distribution data p, and determines the energy E of the incident photons according to the energy spectrum of the gamma photons, reads in the pre-stored left and right singular matrices V, U T and diagonal matrix S, and determines the number N of singular value retention according to the count information M;

[0009] A reconstruction module, which processes the diagonal matrix S according to the number N of singular value retention, and based on C + = VS + U T completes the generalized inverse matrix C of the transfer matrix C of the detection module +Calculate and obtain the reconstructed image based on f = C + p; and

[0010] A display module that displays the reconstructed image f and the energy spectrum and count information M of the incident gamma photons.

[0011] According to an embodiment of the present disclosure, the detection module includes:

[0012] A scintillation crystal that is used to receive incident gamma photons and deposit the gamma photons to obtain a visible light signal,

[0013] An optoelectronic device that is used to convert the visible light signal into an analog signal; and

[0014] A circuit module that converts the analog signal into a digital signal and obtains the position information, energy information, and count information M of the gamma photons.

[0015] According to an embodiment of the present disclosure, the processing module includes:

[0016] A projection statistics module that is used to statistically obtain projection distribution data p of the position information of gamma photons within a certain period of time; an energy extraction module that is used to filter and extract features from the energy spectrum of the incident gamma photons to obtain the photoelectric peak position energy value corresponding to the energy spectrum as the energy E of the incident photons; and

[0017] A reading module that is used to read the corresponding left and right singular matrices V and U T and diagonal matrix S according to the incident photon energy E.

[0018] According to an embodiment of the present disclosure, the processing module further includes:

[0019] A calibration module that is used to calculate the transfer matrix C of the detection module and its generalized inverse matrix C + under different incident photon energies, and perform singular value decomposition on the generalized inverse matrix C + of the transfer matrix C, and obtain and save the obtained left and right singular matrices V and U T and diagonal matrix S.

[0020] According to an embodiment of the present disclosure, the processing module further includes:

[0021] A singular value number determination module that is used to determine the number N of singular values retained in the diagonal matrix S according to the count information M and fitting coefficients k1, k2, and k3:

[0022] N = k1 × M 2 + k2 × M + k3.

[0023] According to an embodiment of the present disclosure, the processing module further includes:

[0024] The fitting coefficient acquisition module is used to obtain the fitting coefficients k1, k2, and k3 by using the least square method based on the quadratic function relationship between the counting information M and the number N of singular value retainments of the diagonal matrix S.

[0025] According to an embodiment of the present disclosure, the reconstruction module includes:

[0026] The diagonal matrix processing module is used to retain the first N eigenvalues of the diagonal matrix S according to the number N of singular value retainments, and set the other eigenvalues to 0;

[0027] The generalized inverse matrix acquisition module is used to calculate the generalized inverse matrix S of the diagonal matrix S + and calculate the generalized inverse matrix C of the transmission matrix C through the generalized inverse matrix S of the diagonal matrix S + ; and + ; and

[0028] The reconstructed image acquisition module is used to calculate the reconstructed image according to the formula f = C + p.

[0029] According to an embodiment of the present disclosure, the gamma detection system further includes:

[0030] The judgment module is used to judge the noise of the reconstructed image. If the noise of the reconstructed image is greater than the threshold, reconstruction is performed again.

[0031] According to an embodiment of the present disclosure, the judgment module includes:

[0032] The error level determination module is used to calculate the overall error level NSD of the reconstructed image f:

[0033]

[0034] where K is the total number of pixels in the image, where f(i) is the activity value of the i-th pixel, is the average value of the image activity; and

[0035] The diagonal matrix update module is used to, when the overall error level NSD of the reconstructed image f is greater than the preset threshold, reduce the number N of singular value retainments, deduct the eigenvalues with smaller singular values, set the other eigenvalues to 0, obtain a new diagonal matrix S, and send it to the generalized inverse matrix acquisition module of the reconstruction module.

[0036] According to an embodiment of the present disclosure, the judgment module further includes:

[0037] The iterative reconstruction module is used to use the reconstructed image f obtained this time +As an initial value, perform ML-EM iteration reconstruction for several times, obtain the reconstructed image again, and send the reconstructed image to the error level determination module.

[0038] According to an embodiment of the present disclosure, the display module includes:

[0039] An alarm module for prompting relevant warning information according to the reconstructed image.

[0040] Another aspect of the present disclosure provides a gamma detection method, which uses the gamma detection system as described above. The gamma detection method includes:

[0041] Receive incident gamma photons, and obtain the position information, energy information, and count information M of the gamma photons;

[0042] Statistically analyze the gamma photons within a certain period of time to obtain their projection distribution data p, and determine the energy E of the incident photons according to the energy spectrum statistically obtained from the energy information, and then read in the pre-stored left and right singular matrices V, U T and diagonal matrix S, and determine the number N of singular values to be retained according to the count information M;

[0043] Process the diagonal matrix S according to the number N of singular values to be retained, based on C + = VS + U T Complete the calculation of the generalized inverse matrix C of the transfer matrix C of the detection module + and calculate the reconstructed image according to f = C + p; and

[0044] Display the reconstructed image f and the energy spectrum and count information M of the incident gamma photons.

[0045] According to an embodiment of the present disclosure, the receiving incident gamma photons and obtaining the position information, energy information, and count information M of the gamma photons includes:

[0046] Receive incident gamma photons, and deposit the gamma photons to obtain a visible light signal,

[0047] Convert the visible light signal into an analog signal; and

[0048] Convert the analog signal into a digital signal, and obtain the position information, energy information, and count information M of the gamma photons.

[0049] Statistically analyze the gamma photons within a certain period of time to obtain their projection distribution data p, and according to an embodiment of the present disclosure, determining the energy E of the incident photons according to the energy spectrum of the incident gamma photons, and reading in the pre-stored left and right singular matrices V, U T and diagonal matrix S includes:

[0050] Filter the energy spectrum of the incident gamma photons and extract features to obtain the energy value of the photoelectric peak corresponding to the energy spectrum as the energy E of the incident photons; and

[0051] Read in the corresponding left and right singular matrices V and U according to the incident photon energy E T and the diagonal matrix S.

[0052] According to an embodiment of the present disclosure, the gamma detection method further includes:

[0053] Calculate the transmission matrix C of the detection module and its generalized inverse matrix C at different incident photon energies + and perform singular value decomposition on the generalized inverse matrix C of the transmission matrix C + to obtain and save the obtained left and right singular matrices V and U T and the diagonal matrix S.

[0054] According to an embodiment of the present disclosure, the determining the number N of singular values to be retained according to the count information M includes:

[0055] Determine the number N of singular values to be retained in the diagonal matrix S according to the count information M and the fitting coefficients k1, k2, and k3:

[0056] N = k1 × M 2 + k2 × M + k3.

[0057] According to an embodiment of the present disclosure, the gamma detection method further includes:

[0058] Obtain the fitting coefficients k1, k2, and k3 by using the least squares method through the quadratic function relationship between the count information M and the number N of singular values to be retained in the diagonal matrix S.

[0059] According to an embodiment of the present disclosure, the completion of the calculation of the generalized inverse matrix C of the transmission matrix C based on C + = VS + U T includes: + Calculate:

[0060] According to the number N of singular values to be retained, retain the first N eigenvalues of the diagonal matrix S and set other eigenvalues to 0;

[0061] Calculate the generalized inverse matrix S of the diagonal matrix S + and calculate the generalized inverse matrix C of the transmission matrix C through the generalized inverse matrix S of the diagonal matrix S + ; and + ; and

[0062] Calculate the reconstructed image according to the formula f = C + p.

[0063] According to an embodiment of the present disclosure, the gamma detection method further includes:

[0064] Judging the noise of the reconstructed image. If the noise of the reconstructed image is greater than a threshold value, reconstruction is performed again.

[0065] According to an embodiment of the present disclosure, the judging the noise of the reconstructed image and performing reconstruction again if the noise of the reconstructed image is greater than a threshold value includes:

[0066] Calculating the overall error level NSD of the reconstructed image f:

[0067]

[0068] where K is the total number of pixels in the image, and f(i) is the activity value of the i-th pixel, is the average value of the image activity; and

[0069] If the overall error level NSD of the reconstructed image f is greater than a preset threshold value, the number N of singular values retained is reduced, the eigenvalues with smaller singular values are deducted, and other eigenvalues are set to 0 to obtain a new diagonal matrix S, and return to calculate the generalized inverse matrix S of the diagonal matrix S + .

[0070] According to an embodiment of the present disclosure, the gamma detection method further includes:

[0071] If the overall error level NSD of the reconstructed image f is greater than a preset threshold value, using the reconstructed image f obtained this time + as an initial value, performing ML-EM iterative reconstruction several times to obtain a reconstructed image again, and sending the reconstructed image to the error level determination module.

[0072] According to an embodiment of the present disclosure, the gamma detection method further includes: prompting relevant warning information according to the reconstructed image.

[0073] Another aspect of the present disclosure provides a gamma detection device, including the gamma detection system as described above.

[0074] According to an embodiment of the present disclosure, the gamma detection device is a handheld gamma detection device, a single photon emission tomography reconstruction device, or a PET reconstruction device.

[0075] (III) Beneficial effects

[0076] It can be seen from the above technical solutions that the gamma detection system, method, and device of the present disclosure have at least one of the following beneficial effects:

[0077] (1) The gamma detection system, method, and device of the present disclosure are based on the singular value decomposition reconstruction algorithm. During real-time imaging, according to different incident gamma photon energies E, the corresponding pre-stored V, U, T , and S can be directly called, and the generalized inverse matrix C of the transfer matrix after retaining the singular value S + is calculated. After that, only one multiplication operation of three matrices C + = VS + U + is performed, and the reconstruction speed is fast, which can be conveniently completed quickly within hardware such as a handheld gamma detection device; 1

[0078] (2) The traditional SVD reconstruction algorithm needs to calculate the singular value decomposition process online in real time, which has too large a computational amount for handheld portable devices and is difficult to implement. The system, method, and device of the present disclosure solve the computational amount problem of singular value decomposition by using a pre-calibration method, determine the relationship between the singular value retention number N and the count information M by using a pre-calibration strategy, and confirm the selection of a reasonable singular value retention number, so as to obtain a high-quality reconstruction result. And the error level NSD is used as a judgment index to dynamically update the signal-to-noise ratio of the image, prevent excessive loss of detailed information in the image when the number of retained singular values is too small, and can judge that when the error level is less than a certain threshold, it can be used as the initial image of the iterative algorithm for reconstruction, and a high-quality image can be obtained quickly and effectively within a certain time. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 is a schematic structural diagram of the gamma detection system according to an embodiment of the present disclosure.

[0080] Figure 2a is a schematic structural diagram of the detection module of the gamma detection system according to an embodiment of the present disclosure.

[0081] Figure 2b is a schematic structural diagram of the processing module of the gamma detection system according to an embodiment of the present disclosure.

[0082] Figure 2c is a schematic structural diagram of the reconstruction module of the gamma detection system according to an embodiment of the present disclosure.

[0083] Figure 3 is a flowchart of the gamma detection method according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0084] ​The present disclosure provides a gamma detection system, including: a detection module, a processing module, a reconstruction module, and a display module. Wherein, the detection module is configured to receive incident gamma photons and obtain the position information, energy information, and count information M of the gamma photons; the processing module statistically processes the gamma photons within a certain period of time to obtain their projection distribution data p, and is configured to determine the energy E of the incident photons according to the energy spectrum of the gamma photons, read in the pre-stored left and right singular matrices V, U T and diagonal matrix S, and determine the number N of singular values to be retained according to the count information M; the reconstruction module is configured to process the diagonal matrix S according to the number N of singular values to be retained, and based on C + = VS + U T complete the calculation of the generalized inverse matrix C of the transfer matrix C of the detection module + and calculate the reconstructed image based on f = C + p; the display module is configured to display the reconstructed image, the energy spectrum of the incident gamma photons, and the count information M

[0085] To make the purpose, technical solutions, and advantages of the present disclosure clearer and more understandable, the following further elaborates on the present disclosure in detail with reference to specific embodiments and the accompanying drawings

[0086] Some embodiments of the present disclosure will be described more comprehensively hereinafter with reference to the accompanying drawings, and some but not all of the embodiments will be shown. In fact, various embodiments of the present disclosure can be implemented in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided to enable the present disclosure to meet applicable legal requirements

[0087] In an exemplary embodiment of the present disclosure, a gamma detection system 100 is provided

[0088] Figure 1 is a schematic structural diagram of the gamma detection system according to an embodiment of the present disclosure. As Figure 1 shown, the gamma detection system 100 of the present disclosure includes a detection module 110, a processing module 120, a reconstruction module 130, and a display module 140

[0089] Wherein, the detection module 110 is configured to receive incident gamma photons and obtain the position information, energy information, and count information M of the gamma photons

[0090] Figure 2a is a schematic structural diagram of the detection module of the gamma detection system according to an embodiment of the present disclosure. As Figure 2aAs shown, the detection module 110 includes a scintillation crystal 111, a photoelectric device 112, and a circuit module 113. The scintillation crystal 111 is configured to receive incident gamma photons and deposit the gamma photons to obtain a visible light signal. The photoelectric device 112 is configured to convert the visible light signal into an analog signal. The circuit module 113 converts the analog signal into a digital signal and acquires the position information, energy information, and count information M of the gamma photons.

[0091] The processing module 120 is configured to statistically obtain projection distribution data p based on the position information of gamma photons within a certain period of time; determine the energy E of the incident photons based on the energy spectrum statistically obtained from the gamma photon energy information, and thereby read the pre-stored left and right singular matrices V and U T and the diagonal matrix S, and determine the number N of singular values to be retained according to the count information M.

[0092] Figure 2b It is a schematic structural diagram of the detection module of the gamma detection system according to an embodiment of the present disclosure. As Figure 2b shown, the processing module 120 includes a projection statistics module 121', an energy extraction module 121, a reading module 122, and a singular value number determination module 123.

[0093] Among them, the projection statistics module 121' is configured to statistically obtain projection distribution data p based on the position information of gamma photons within a certain period of time. The energy extraction module 121 is configured to filter and extract features from the energy spectrum of the incident gamma photons to obtain the photoelectric peak position energy value corresponding to the energy spectrum as the energy E of the incident photons. The reading module 122 is configured to read the corresponding pre-stored left and right singular matrices V and U T and the diagonal matrix S according to the energy E of the incident photons. The singular value number determination module 123 is configured to determine the number N of singular values to be retained in the diagonal matrix S.

[0094] The reconstruction algorithm adopted in the embodiment of the present disclosure is based on singular value decomposition, and only one multiplication operation of three matrices is performed during the reconstruction process C + = VS + U T . However, the singular value decomposition reconstruction algorithm needs to determine the number of singular values to be retained according to the actually measured count information M, and the computational amount and storage amount of the singular value decomposition process of the transfer matrix are both very large and cannot be completed within a handheld hardware. Therefore, the present disclosure adopts a pre-calibration method to solve the problem of the computational amount of singular value decomposition, and then adopts a pre-calibration strategy to determine the relationship between the number of singular values to be retained and the count information, confirm and select a reasonable number N of singular values to be retained. The diagonal matrix S can obtain a high-quality reconstruction result by retaining N singular values. During real-time imaging, the corresponding V, U, and S can be directly called, and the generalized inverse matrix of the transfer matrix after retaining the singular values can be directly calculated, thereby realizing real-time reconstruction within the hardware.

[0095] Specifically, in order to reduce the computational load of the reconstruction process, the processing module 120 is further configured to implement prior calibration work. For prior calibration, the processing module 120 includes a calibration module 124 for calculating the transmission matrix C of the detection module and its generalized inverse matrix C + of the transmission matrix C, and + performing singular value decomposition on the generalized inverse matrix C T of the transmission matrix C to obtain and save the left and right singular matrices V and U T and the diagonal matrix S. Thus, after obtaining the incident photon energy E in the current image reconstruction process, the pre-stored left and right singular matrices V and U

[0096] at this incident photon energy and T the diagonal matrix S can be correspondingly read, so as to achieve fast reconstruction.

[0096] After obtaining the left and right singular matrices V and U T and the diagonal matrix S, it is also necessary to determine the number N of singular values to be retained in the diagonal matrix S. To this end, the singular value number determination module 123 determines the number N of singular values to be retained in the diagonal matrix S according to the counting information M and the fitting coefficients k1, k2, and k3:

[0097] N = k1 × M 2 + k2 × M + k3

[0098] wherein, the counting information M can be the total count of the projection image, or the average value, variance, etc. of the projection counts. The selection of the number N of singular values to be retained is to obtain the best reconstructed image f after processing the diagonal matrix S.

[0099] To achieve real-time reconstruction, the fitting coefficients k1, k2, and k3 are also pre-stored values. Therefore, the processing module 120 may further include a fitting coefficient acquisition module 125 for obtaining the fitting coefficients k1, k2, and k3 by using the least squares method through the quadratic function relationship between the counting information M and the number N of singular values to be retained in the diagonal matrix S.

[0100] The reconstruction module 130 processes the diagonal matrix S according to the number N of singular values to be retained, and based on C + = VS + U T completes the calculation of the generalized inverse matrix C + of the transmission matrix C of the detection module, and calculates the reconstructed image according to f = C + p.

[0101] Figure 2cThis is a schematic structural diagram of the detection module of the gamma detection system according to an embodiment of the present disclosure. As shown in FIG. 2C, the reconstruction module 130 includes a diagonal matrix processing module 131, a generalized inverse matrix acquisition module 132, and a reconstructed image acquisition module 133. Among them, the diagonal matrix processing module 131 is configured to retain the first N eigenvalues of the diagonal matrix S according to the number N of singular value retainments, and set the other eigenvalues to 0. The generalized inverse matrix acquisition module 132 is configured to calculate the generalized inverse matrix S + , where the diagonal element value of S + is the reciprocal of the corresponding position of S, and the element values of other positions are 0. The generalized inverse matrix acquisition module 132 then calculates the generalized inverse matrix C + of the transmission matrix C through the generalized inverse matrix S+ of the diagonal matrix S. The reconstructed image acquisition module 133 is configured to calculate the reconstructed image according to the formula f = C + p.

[0102] Exemplarily, the imaging process of the system can be expressed as

[0103] Cf = p

[0104] where C is the transmission matrix of the detection module, f is the radioactive concentration distribution represented in vector form, and p is the projection represented in vector form. According to the singular value decomposition theorem, it can be written as

[0105] USV T f = p

[0106] Or

[0107] SV T f = U T p

[0108] Both U and V in the formula are orthogonal matrices. Regarding V T f and U T p as coordinate transformations of the projection vector p and the image vector f in the projection space and the image space respectively. After the above coordinate transformation, the imaging process of the detection module depends on the matrix S composed of the singular values of the transmission matrix C, which reflects the main characteristics of the transmission matrix C of the detection module.

[0109] Let

[0110] S + = [∑ -1 0]

[0111] where

[0112]

[0113] r is the number of non-zero singular values of C, and let

[0114] C += VS + U T

[0115] C + is called the generalized matrix inverse of C. It can be proved that

[0116] f = C + p = VS + U T p

[0117] Assume that the detection module transmission matrix C does not contain zero singular values (which is in line with the actual situation). Therefore, the above formula can be written as

[0118]

[0119] where (U T p) is the i-th component of the projected vector (U T p) after coordinate transformation.

[0120] After obtaining the singular value decomposition result of the system transmission matrix, the reconstruction process only needs to calculate the multiplication of the formulas f = C + p = VS + U T p, so the reconstruction speed is very fast.

[0121] As the number of retained singular values decreases, the influence of noise gradually decreases and the image quality gets better and better. However, when the number of retained singular values is too small, too much detailed information of the image is lost and the image noise level index may become worse again.

[0122] To address this problem, the gamma detection system of the present disclosure further includes a judgment module 134. After the reconstruction image acquisition module 133 obtains the reconstructed image, the judgment module 134 can judge the noise of the reconstructed image and confirm whether another reconstruction is needed. By using the overall error level NSD as the judgment index, the signal-to-noise ratio of the image is dynamically updated.

[0123] Specifically, the judgment module 134 includes an error level determination module and a diagonal matrix update module. The error level determination module is used to calculate the overall error level NSD of the reconstructed image f:

[0124]

[0125] where K is the total number of pixels in the image, and f(i) is the activity value of the i-th pixel, is the average value of the image activity;

[0126] The diagonal matrix update module is used to reduce the number N of singular value retainments, subtract the eigenvalues with smaller singular values, and set other eigenvalues to 0 to obtain a new diagonal matrix S when the overall error level NSD of the reconstructed image f is greater than a preset threshold, and send it to the generalized inverse matrix acquisition module 132 of the reconstruction module 130.

[0127] Furthermore, when the image difference reaches a certain level, the iterative algorithm can also be used for reconstruction, that is, the reconstruction module 130 can further include an iterative reconstruction module 131, which is used to use the reconstructed image f obtained this time as the initial value to perform several ML-EM iterative reconstructions, obtain the reconstructed image again, and send the reconstructed image to the error level determination module. Thus, real-time and high-quality image reconstruction can be effectively achieved on effective hardware resources.

[0128] To achieve accurate visual positioning of the radioactive material distribution and quickly and accurately find the radiation source, the gamma detection system of the present disclosure embodiment further includes a display module for displaying the reconstructed image and the energy spectrum and count information M of the incident gamma photons.

[0129] Furthermore, the gamma detection system of the present disclosure embodiment further includes an alarm module for prompting relevant warning information according to the reconstructed image.

[0130] In another exemplary embodiment of the present disclosure, a gamma detection method is provided.

[0131] Figure 3 As shown in the flowchart of the gamma detection method of the present disclosure embodiment, Figure 3 as described, the gamma detection method includes operations S310-S340.

[0132] In operation S310, incident gamma photons are received, and gamma photon position information, energy information, and count information M are obtained;

[0133] In operation S320, according to the energy E of the incident photons obtained from the energy spectrum of the incident gamma photons, the pre-stored left and right singular matrices V and U T and the diagonal matrix S are read, and the number N of singular value retainments is determined according to the count information M;

[0134] In operation S330, the diagonal matrix S is processed according to the number N of singular value retainments, and according to C + =VS + U T the generalized inverse matrix C of the transfer matrix C is completed + calculation, and the reconstructed image is calculated according to f = C + p; and

[0135] In operation S340, the reconstructed image and the energy spectrum and count information M of the incident gamma photons are displayed.

[0136] Further, the gamma detection method further includes an operation S350 to prompt relevant warning information according to the reconstructed image.

[0137] Specifically, in operation S310, receiving the incident gamma photons and obtaining the projection distribution data p, energy spectrum, and count information M of the gamma photons includes operations S311 - S313.

[0138] In operation S311, receiving the incident gamma photons and depositing the gamma photons to obtain a visible light signal;

[0139] In operation S312, converting the visible light signal into an analog signal; and

[0140] In operation S313, converting the analog signal into a digital signal and obtaining the projection distribution data p, energy spectrum, and count information M of the gamma photons.

[0141] In operation S320, for the energy E of the incident photons obtained from the energy spectrum of the incident gamma photons, reading in the pre - stored left and right singular matrices V, U T and diagonal matrix S includes operations S321 - S322.

[0142] In operation S321, filtering and feature extraction are performed on the energy spectrum of the incident gamma photons to obtain the energy value of the photoelectric peak corresponding to the energy spectrum as the energy E of the incident photons; and

[0143] In operation S322, reading in the corresponding left and right singular matrices V and U according to the incident photon energy E 1 and diagonal matrix S.

[0144] In operation S323, determining the number N of singular values to be retained in the diagonal matrix S according to the count information M and fitting coefficients k1, k2, and k3:

[0145] N = k1×M 2 +k2×M + k3

[0146] Wherein, the count information M can be the total count of the projection image or the average value, variance, etc. of the projection counts. The fitting coefficients k1, k2, and k3 are obtained by using the least - squares method for the quadratic function relationship between the count information M and the number N of singular values to be retained in the diagonal matrix S.

[0147] Further, before operation S320, a calibration operation is also included, including:

[0148] Calculating the transfer matrix C of the detection module at different incident photon energies;

[0149] Calculating the generalized inverse matrix C of the transfer matrix C of the detection module+ ; and

[0150] Perform a singular value decomposition on the generalized inverse matrix C of the transmission matrix C + to obtain and save the left and right singular matrices V and U T and the diagonal matrix S.

[0151] In operation S330, it specifically includes operations S331 - S333.

[0152] In operation S331, according to the number of singular values to be retained N, retain the first N eigenvalues of the diagonal matrix S, and set the other eigenvalues to 0;

[0153] In operation S332, calculate the generalized inverse matrix S of the diagonal matrix S + , and calculate the generalized inverse matrix C of the transmission matrix C through the generalized inverse matrix S of the diagonal matrix S + ; and + ; and

[0154] In operation S333, calculate the reconstructed image according to the formula f = C + p.

[0155] Furthermore, operation S330 also includes operation S334, which judges the noise of the reconstructed image. If the noise of the reconstructed image is greater than the threshold, reconstruction is performed again, including:

[0156] Calculate the overall error level NSD of the reconstructed image f:

[0157]

[0158] where K is the total number of pixels in the image, and f(i) is the activity value of the i-th pixel, is the average value of the image activity.

[0159] If the overall error level NSD of the reconstructed image f is greater than the preset threshold, reduce the number of singular values to be retained N, subtract the eigenvalues with smaller singular values, and set the other eigenvalues to 0 to obtain a new diagonal matrix S, and return to calculate the generalized inverse matrix S of the diagonal matrix S + .

[0160] Operation S330 also includes operation S335. If the overall error level NSD of the reconstructed image f is greater than the preset threshold, use the reconstructed image f obtained this time as the initial value, perform several ML-EM iterative reconstructions, obtain the reconstructed image again, and return to calculate the error level NSD.

[0161] In another exemplary embodiment of the present disclosure, a gamma detection device is provided, including the gamma detection system as described in the foregoing examples. The applicable scope of the gamma detection device includes, but is not limited to, handheld hardware devices in the field of nuclear radiation detection and gamma imaging, and can also be applied to single-photon emission tomography reconstruction devices or PET reconstruction devices in the field of nuclear medicine.

[0162] Exemplarily, the handheld gamma detection device according to the embodiment of the present disclosure can highly integrate functions such as metal detection, body temperature measurement, and substance detection, and become a handheld device with comprehensive functions.

[0163] So far, the embodiments of the present disclosure have been described in detail with reference to the accompanying drawings. It should be noted that in the accompanying drawings or the text of the specification, the implementation manners that are not depicted or described are all forms known to those of ordinary skill in the art, and no detailed description is given. In addition, the definitions of the above-mentioned various elements and methods are not limited to the specific structures, shapes, or manners mentioned in the embodiments, and those of ordinary skill in the art can simply modify or replace them.

[0164] Furthermore, the word "comprising" does not exclude the existence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the existence of a plurality of such elements.

[0165] In addition, unless specifically described or steps that must occur in sequence, the order of the above steps is not limited to those listed above, and can be changed or rearranged according to the required design. And the above embodiments can be used in combination with each other or combined with other embodiments based on considerations of design and reliability, that is, the technical features in different embodiments can be freely combined to form more embodiments.

[0166] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings based herein. The structure required to construct such a system is obvious from the above description. In addition, the present disclosure is not directed to any particular programming language. It should be understood that the content of the present disclosure described herein can be implemented using various programming languages, and the description of the specific language above is for disclosing the best mode of the present disclosure.

[0167] The present disclosure can be implemented by means of hardware including several different elements and by means of a computer programmed appropriately. Each component embodiment of the present disclosure can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the relevant devices according to the embodiments of the present disclosure. The present disclosure can also be implemented as a device or apparatus program (for example, a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present disclosure can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0168] Those skilled in the art can understand that the modules in the devices in the embodiments can be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all the features disclosed in this specification (including the accompanying claims, abstract and drawings) and all the processes or units of any method or device so disclosed. Unless otherwise explicitly stated, each feature disclosed in this specification (including the accompanying claims, abstract and drawings) can be replaced by an alternative feature providing the same, equivalent or similar purpose. And, in the unit claims listing several devices, several of these devices can be embodied by the same hardware item.

[0169] Similarly, it should be understood that, in order to streamline the present disclosure and help understand one or more of the various disclosed aspects, in the above description of the exemplary embodiments of the present disclosure, the various features of the present disclosure are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be construed as reflecting an intention that the claimed present disclosure requires more features than those expressly recited in each claim. Rather, as reflected in the following claims, the disclosed aspects lie in less than all the features of the single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into the detailed description, where each claim itself serves as a separate embodiment of the present disclosure.

[0170] The specific embodiments described above further elaborate on the objective, technical solutions, and beneficial effects of the present disclosure. It should be understood that the above are only specific embodiments of the present disclosure and are not intended to limit the present disclosure. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A gamma detection system, characterized in that, Comprising: A detection module, configured to receive incident gamma photons, and acquire the position information, energy information and count information M of the gamma photons; A processing module that statistically analyzes gamma photons within a certain period of time to obtain their projection distribution data p, determines the energy E of incident photons based on the energy spectrum statistically obtained from the energy information, and reads in the pre-stored left and right singular matrices V and U accordingly T and a diagonal matrix S. Among them, the processing module includes a singular value number determination module for determining the number N of singular values retained in the diagonal matrix S according to the counting information M and fitting coefficients k1, k2, and k3 ； A reconstruction module processes the diagonal matrix S according to the number N of singular values to be retained, based on C + = VS + U T Complete the generalized inverse matrix C of the transmission matrix C of the detection module + Calculate, and based on f = C + Calculate the reconstructed image according to p; and A display module, configured to display the reconstructed image f and the energy spectrum and count information M of the incident gamma photons.

2. The gamma detection system according to claim 1, wherein The detection module comprises: A scintillation crystal, which is configured to receive incident gamma photons and deposit the gamma photons to obtain a visible light signal; An optoelectronic device, which is configured to convert the visible light signal into an analog signal; and A circuit module, which is configured to convert the analog signal into a digital signal, and acquire the position information, energy information and count information M of the gamma photons.

3. The gamma detection system according to claim 1, characterized in that, The processing module comprises: A projection statistics module, configured to statistically acquire projection distribution data p based on the position information of gamma photons within a certain period of time; An energy extraction module, configured to filter and extract features from the energy spectrum of the incident gamma photons, and obtain the photoelectric peak position energy value corresponding to the energy spectrum as the energy E of the incident photons; and A reading module, configured to read in corresponding left and right singular matrices V and U according to the incident photon energy E T and a diagonal matrix S.

4. The gamma detection system according to claim 3, characterized in that, The processing module further comprises: The calibration module is used to calculate the transmission matrix C of the detection module and its generalized inverse matrix C under different incident photon energies + and perform singular value decomposition on the generalized inverse matrix C of the transmission matrix C + to obtain and save the left and right singular matrices V and U T and the diagonal matrix S.

5. The gamma detection system according to claim 1, wherein The processing module further comprises: A fitting coefficient acquisition module, configured to obtain fitting coefficients k1, k2 and k3 by using the least square method based on the quadratic function relationship between the count information M and the number N of retained singular values of the diagonal matrix S.

6. The gamma detection system according to claim 1, characterized in that, The reconstruction module comprises: A diagonal matrix processing module, configured to retain the first N eigenvalues of the diagonal matrix S according to the number N of retained singular values, and set the other eigenvalues to 0; Generalized inverse matrix acquisition module, used to calculate the generalized inverse matrix S of the diagonal matrix S + , and calculate the generalized inverse matrix C of the transfer matrix C through the generalized inverse matrix S + of the diagonal matrix S; and + ; and A reconstructed image acquisition module, which is used to obtain a reconstructed image according to the formula f = C + p to calculate the reconstructed image.

7. The gamma detection system according to claim 1, characterized in that, Further comprising: A judgment module, configured to judge the noise of the reconstructed image. If the noise of the reconstructed image is greater than the threshold, reconstruction is performed again. The judgment module comprises: An error level determination module, configured to calculate the overall error level NSD of the reconstructed image f; where K is the total number of pixels in the image, and where is the activity value of the i-th pixel, is the average value of the image activity, and A diagonal matrix update module, configured to, when the overall error level NSD of the reconstructed image f is greater than a preset threshold, reduce the number N of retained singular values, deduct the eigenvalues with smaller singular values, set the other eigenvalues to 0, obtain a new diagonal matrix S, and send it to the generalized inverse matrix acquisition module of the reconstruction module.

8. The gamma detection system according to claim 7, characterized in that, The judgment module further comprises: An iterative reconstruction module, configured to use the obtained reconstructed image f as an initial value to perform several times of ML-EM iterative reconstruction, obtain the reconstructed image again, and send the reconstructed image to the error level determination module.

9. A gamma detection method, characterized in that, Using the gamma detection system according to any one of claims 1-8, the gamma detection method comprises: Receiving incident gamma photons, and acquiring the position information, energy information and count information M of the gamma photons; Statistically obtain the projection distribution data p of gamma photons within a certain period of time, and determine the energy E of the incident photons according to the energy spectrum statistically obtained from the energy information, and thereby read in the pre-stored left and right singular matrices V and U T and the diagonal matrix S, and determine the number N of singular values retained in the diagonal matrix S according to the count information M and the fitting coefficients k1, k2, and k3: ； Process the diagonal matrix S according to the number N of singular values retained, based on C + = VS + U T Complete the calculation of the generalized inverse matrix C of the transmission matrix C of the detection module, and based on f = C + Calculate, and calculate the reconstructed image based on f = C + p; and Displaying the reconstructed image f and the energy spectrum and count information M of the incident gamma photons.

10. A handheld gamma detection device, characterized in that, Comprising the gamma detection system according to any one of claims 1-8.

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