Gamma-ray spectrum high-energy resolution reconstruction method, storage medium and equipment

Abstract

The invention discloses a gamma-ray energy spectrum high-energy resolution reconstruction method, a storage medium and equipment, and the method comprises the steps: obtaining a gamma-ray energy spectrum signal through a NaI (T1) detector, carrying out the energy spectrum calibration of the obtained gamma-ray energy spectrum signal, and obtaining a gamma-ray energy spectrum with the energy spectrum calibration; constructing a gamma-ray energy spectrum response matrix for the gamma-ray energy spectrum with the energy spectrum scales through Monte Carlo modeling; the method comprises the following steps: performing energy spectrum compression on a gamma-ray energy spectrum signal acquired by a NaI (T1) detector through a gamma-ray energy spectrum response matrix, and preprocessing a gamma-ray energy spectrum after energy spectrum compression; and performing energy spectrum reconstruction on the preprocessed gamma-ray energy spectrum by adopting an inverse solution algorithm to obtain a reconstructed original emergent gamma-ray energy spectrum, and performing nuclide analysis. Energy spectrum reconstruction with high energy resolution is realized through a reconstruction algorithm, and the high energy resolution of the gamma-ray energy spectrum is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of photoelectric detection and radiation detection, and in particular, relates to a gamma spectrum high-energy resolution reconstruction method, storage medium and device. Background Art

[0002] NaI(Tl) detectors have the characteristics of high detection efficiency and wide energy range, and are suitable for detecting gamma rays and X-rays. NaI(Tl) detectors are widely used in nuclear physics research, medical imaging, nuclear medicine, and nuclear radiation monitoring. By measuring the intensity and time of the scintillation light in the detector, the type and energy of radiation can be determined. Therefore, NaI(Tl) detectors play an important role in radiation detection and radiation measurement. In the analysis of internal irradiation nuclides, nuclide identification and peak area calculation are two key technologies. Due to the low energy resolution of the commonly used NaI(TI) scintillator detectors, and considering the low activity of the nuclides in the body of the patient to be tested, the photon counts obtained by the detector during the detection process are low. These reasons make the existing nuclide analysis methods have the problem of misidentification of radioactive nuclides, missed counts, and large errors in calculating the full-energy peak area when applied to the analysis of internal irradiation nuclides.

[0003] The total energy peak method is usually used to analyze the gamma spectrum, but due to the poor quality of the NaI (Tl) detector, the results obtained by this method are very easy to be misidentified and missed, and the peak fitting has large errors due to repeated peaks and high background. In recent years, based on the energy spectrum reconstruction algorithm, the system matrix directly from the nuclide emission spectrum to the detector measurement spectrum is established, and then the analysis technology of inverting the emission spectrum from the measurement spectrum using a special iterative algorithm has been increasingly applied. Currently, the main ones used include the Gold algorithm and the EM algorithm. This type of method has the advantage of non-negative solution values, but it is difficult to completely decompose the overlapping peaks, and the reconstruction results cannot achieve direct reading of quantitative results. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a method, storage medium and device for reconstructing a gamma energy spectrum with high energy resolution, which realizes energy spectrum reconstruction with high energy resolution through a reconstruction algorithm and improves the high energy resolution of the gamma energy spectrum.

[0005] To achieve the above technical objectives, the present invention adopts the following technical solution: a gamma spectrum high energy resolution reconstruction method, specifically comprising the following steps:

[0006] Step 1, acquiring a gamma energy spectrum signal by a NaI (Tl) detector, performing energy spectrum calibration on the acquired gamma energy spectrum signal to obtain a gamma energy spectrum with energy spectrum calibration, and calculating a broadening coefficient;

[0007] Step 2: According to the gamma spectrum with energy spectrum scale and the broadening coefficient, the gamma spectrum response matrix is ​​constructed in combination with the Monte Carlo method to obtain the gamma spectrum response matrix;

[0008] Step 3, compressing the gamma energy spectrum signal acquired by the NaI (Tl) detector through the gamma energy spectrum response matrix, and preprocessing the gamma energy spectrum after the energy spectrum compression;

[0009] Step 4: Reconstruct the preprocessed gamma energy spectrum using an inverse solution algorithm to obtain a reconstructed original emission gamma energy spectrum for nuclide analysis.

[0010] Furthermore, in step 1, a gamma energy spectrum signal is obtained by a NaI (Tl) detector, and then the energy spectrum is calibrated to obtain a discretized gamma detection energy spectrum y (n):

[0011]

[0012] Where n' is the channel address of the emitted gamma energy spectrum, N' is the channel address number, n is the channel address of the measured energy spectrum detected by the NaI(Tl) detector, N is the channel address number of the measured energy spectrum, x(n') is the emitted energy spectrum of the n'th energy in the gamma energy spectrum, and r(n'→n) is the unit response of the n'th energy in the gamma energy spectrum at the nth energy point in the measured energy spectrum.

[0013] Furthermore, the specific process of step 2 is as follows: a detector model is constructed according to the size of the NaI(Tl) detector in the Monte Carlo model, and the characteristic peak broadening, energy counting interval and the number of particles in the energy counting interval obtained by measuring the gamma energy spectrum with an energy spectrum scale are used as inputs of the detector model to obtain the gamma energy spectrum response matrix.

[0014] Furthermore, the compression process of the gamma spectrum in step 3 is:

[0015] y=Rx

[0016] Among them, y is the compressed gamma energy spectrum, x is the original output gamma energy spectrum to be solved, and R is the gamma energy spectrum response matrix.

[0017] Furthermore, the process of preprocessing the gamma spectrum after the spectrum compression in step 3 includes: spectrum smoothing and spectrum background subtraction.

[0018] Furthermore, step 4 includes the following sub-steps:

[0019] Step 4.1: Initialize the residual value Index Set Iteration number t = 1, original gamma spectrum sparsity k, where, is an empty set, is the gamma spectrum after preprocessing;

[0020] Step 4.2: Calculate the index value Where N represents the number of columns of the gamma spectrum response matrix, j represents the index of N, and r j represents the jth column in the gamma spectrum response matrix, |<f t-1 ,r j >| indicates calculation of f t-1 With r j The inner product value of ;

[0021] Step 4.3: Update the index set Σ according to the calculated index value t =Σ t-1 ∪{ε t}, R t =R t-1 ∪r t , where R t Represents the index value ε obtained after the tth iteration t The set of all columns of the selected gamma spectrum response matrix R, r t Indicates that the tth iteration is based on the index value ε t Select the column elements of the gamma spectrum response matrix R;

[0022] Step 4.4: Solve using the least squares method make Among them, x t represents a column vector of t×1;

[0023] Step 4.5: According to Update residual value Update the number of iterations t = t + 1;

[0024] Step 4.6: If the updated number of iterations t≤k, repeat steps 4.2-4.5; otherwise, as the reconstructed raw outgoing gamma spectrum.

[0025] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the gamma spectrum high-energy resolution reconstruction method.

[0026] Furthermore, the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for reconstructing the gamma spectrum with high energy resolution is implemented.

[0027] Compared with the prior art, the present invention has the following beneficial effects: using the gamma spectrum high energy resolution reconstruction algorithm of the present invention and using the Monte Carlo method to construct the gamma spectrum response matrix is ​​an accurate and full use of the full spectrum information of the gamma spectrum. At the same time, in the process of constructing the gamma spectrum response matrix, the physical and geometric information in the entire measurement scene is also included. In theory, a better solution can be obtained by inversely solving the response matrix. In addition, compared with the experiment, the use of the Monte Carlo method has extremely low cost, high safety, and flexible use. By adjusting the parameters, it can basically match the actual model. The reconstructed original emission gamma spectrum has a more accurate peak position identification effect and full energy peak area calculation accuracy, achieving the highest nuclide activity calculation accuracy at present. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of the gamma spectrum high energy resolution reconstruction method of the present invention;

[0029] Figure 2 This is a flow chart of energy spectrum reconstruction in the present invention. DETAILED DESCRIPTION

[0030] The technical solution of the present invention is further explained below in conjunction with the accompanying drawings.

[0031] like Figure 1 This is a flow chart of the gamma spectrum high energy resolution reconstruction method of the present invention, which specifically includes the following steps:

[0032] Step 1: Obtain a gamma energy spectrum signal from a NaI (Tl) detector, perform energy spectrum calibration on the obtained gamma energy spectrum signal to obtain a gamma energy spectrum with energy spectrum calibration, and calculate a broadening coefficient;

[0033] Since the original gamma emission energy spectrum required by the system is x(E'), the gamma detection energy spectrum obtained by the detector is y(E), and r(E'→E) is used to represent the energy response of photons with incident energy E' in the measured energy spectrum E, the process can be expressed as:

[0034]

[0035] Among them, E0 and E max The energy ranges of the gamma energy spectrum are respectively represented. If the energy calibration is not performed, the energy spectrum obtained by the NaI (Tl) detector is the relationship between the count and the channel address, and the relationship between the energy spectrum characteristic value and the energy cannot be read. The energy calibration in the present invention adopts the experimental measurement method, using 241 Am, 137 Cs, 60 The radionuclides contained in the three radioactive sources of Co and the NaI(Tl) detector itself 208Tl calibrates the NaI (Tl) detector. After the calibration is completed, the energy spectrum obtained by the detection is expressed as an energy channel address relationship, and each channel address corresponds to an energy.

[0036] Therefore, the gamma energy spectrum signal is obtained by the NaI (Tl) detector and the discretized gamma detection energy spectrum y (n) is obtained after the energy spectrum is calibrated:

[0037]

[0038] Where n' is the channel address of the emitted gamma energy spectrum, N' is the channel address number, n is the channel address of the measured energy spectrum detected by the NaI(Tl) detector, N is the channel address number of the measured energy spectrum, x(n') is the emitted energy spectrum of the n'th energy in the gamma energy spectrum, and r(n'→n) is the unit response of the n'th energy in the gamma energy spectrum at the nth energy point in the measured energy spectrum.

[0039] Step 2: The elements of the gamma spectrum response matrix R are the unit responses r(n'→n) of the n'th energy in the gamma spectrum at the n'th energy point in the measured spectrum. Each column of the gamma spectrum response matrix R is (r(n'→1), r(n'→2), r(n'→3), ..., r(n'→N)), which represents the complete measured spectrum with the incident energy of the gamma ray being n'. Let y N' =(r(n'→1),r(n'→2),r(n'→3),…,r(n'→N)). In acquiring the gamma energy spectrum response matrix, it is only necessary to obtain the complete detection energy spectrum data y1,…,y of each energy represented by the gamma ray in 1 to N'. N' That is. The Monte Carlo method is a method that reflects the probability of occurrence through statistical events, and can simulate the physical movement and transportation process of particles. This method can simulate the corresponding effects of different parameter changes on the detector by changing the material parameters, geometric dimensions and electronic parameters of each part of the detector, thereby realizing the collection simulation of the effect between the detector and the particles, and providing effective data support for the optimization of detector selection, performance evaluation, etc. Therefore, the present invention constructs a gamma energy spectrum response matrix based on the gamma energy spectrum and broadening coefficient with an energy spectrum scale, combined with the Monte Carlo method, to obtain a gamma energy spectrum response matrix; the energy of the source term radiation source can be set as required, and the statistical error can be reduced by increasing the number of particles in the Monte Carlo. Specifically, in the Monte Carlo model, a detector model is constructed according to the size of the NaI (Tl) detector, and the characteristic peak broadening, energy counting interval and number of particles in the energy counting interval obtained by measuring the gamma energy spectrum with an energy spectrum scale are used as inputs of the detector model to obtain a gamma energy spectrum response matrix. The characteristic peak broadening in the gamma energy spectrum is obtained by experimental measurement. The energy counting range is 15keV~3MeV, corresponding to N channels, and N' energy points are calculated at equal intervals in the energy counting interval. The number of particles simulated each time is 1×108 , Since the monoenergetic gamma spectrum obtained by simulation at each energy point is used as the corresponding column data of the response matrix, the gamma spectrum response matrix R is finally obtained, and the shape is n×N'.

[0040] Step 3, the original emitted gamma spectrum of the radioactive nuclide without any scattering has amplitude only at a few isolated energy points, and is zero at other energies, which shows that the gamma-ray emission spectrum meets the sparsity requirements. Therefore, the gamma spectrum signal obtained by the NaI (Tl) detector is compressed by the gamma spectrum response matrix, and the gamma spectrum after the compressed spectrum is preprocessed; the process of preprocessing the gamma spectrum after the compressed spectrum includes: spectrum smoothing and spectrum background subtraction. Due to the natural environment background radiation and gamma spectrum, the Compton plateau generated by these incident gamma energy photons is superimposed with the characteristic peak during the detection process, which seriously interferes with the quantitative analysis of the spectrum; at the same time, statistical fluctuations will occur in the measurement process, and the actual count will deviate from the theoretical count. Through preprocessing, the influence of factors such as the difference in specifications of two or more detectors in the detector system, system noise, statistical fluctuations in the detection process, and scattering background can be solved, the probability of false peaks appearing during the spectrum analysis process can be reduced, the influence on the subsequent spectrum analysis can be reduced, and the accuracy of the inverse data can be ensured.

[0041] The compression process of the gamma energy spectrum in the present invention is:

[0042]

[0043] Where y is the compressed gamma spectrum, is the gamma spectrum signal to be solved, and R is the gamma spectrum response matrix.

[0044] For the original ideal energy spectrum response solution problem, the sparsity expression is incorporated into the calculation process, and the gamma energy spectrum reconstruction process solution problem can be converted into a constrained extreme value solution problem:

[0045]

[0046] If x∈{x1,x2,…,x i ,…,x n}, then ||x|| p The norm can be expressed as follows:

[0047]

[0048] In the present invention, in order to express the sparsity of the original ideal energy spectrum, p=0 is taken, which represents the isolated energy points in the energy spectrum, that is, to solve the gamma energy spectrum that satisfies the system response relationship with the sparsest representation. The present invention uses a regularization idea to approximate the sparse solution by selecting a column that is orthogonal to the residual and has the largest correlation in each iteration. The constrained extreme value problem is further simplified to an extreme value problem without conditional constraints. The specific derivation process is as follows:

[0049] (1) Construct the residual function so that the equation As far as possible, the residual The 2-norm of is the smallest, that is, Here, the 2-norm represents the energy spectrum x and the gamma spectrum response matrix R, which is different from the detected energy spectrum data. The sum of squared deviations;

[0050] (2) 2-norm expansion:

[0051] (3) Take the partial derivative of the residual function and calculate the minimum value:

[0052] (4) Setting the partial derivative equal to 0, we obtain:

[0053] The core principle of this method is to treat the gamma energy spectrum response matrix R obtained by the above simulation as a sparse dictionary to be solved. If the sparsity of the original ideal energy spectrum of the input energy spectrum information is k, the task of the algorithm is to find the column vector of the response matrix R with the smallest residual value in the previous step in the loop iteration, that is, to obtain the subspace composed of the most relevant k vectors and minimize the objective function in this subspace.

[0054] Step 4: Reconstruct the preprocessed gamma spectrum using an inverse solution algorithm to obtain the reconstructed original emission gamma spectrum for nuclide analysis. This method is relatively easy to implement, especially for sparse gamma spectrum, which has good reconstruction performance and computational efficiency, can effectively restore the characteristic peak position and height of the gamma spectrum, and has advantages in reducing computational complexity and time cost. Figure 2 , specifically including the following sub-steps:

[0055] Step 4.1: Initialize the residual value Index Set Iteration number t = 1, original gamma spectrum sparsity k, where, is an empty set, is the gamma spectrum after preprocessing;

[0056] Step 4.2: Calculate the index value Where N represents the number of columns of the gamma spectrum response matrix, j represents the index of N, and r j represents the jth column in the gamma spectrum response matrix, | <f t-1 ,r j >| indicates calculation of f t-1 With r j The inner product value is to find the residual f t-1 The column vector index value ε of the gamma spectrum response matrix that best matches t ;

[0057] Step 4.3: Update the index set Σ according to the calculated index value t =Σ t-1 ∪{ε t}, R t =R t-1 ∪r t , where R t Represents the index value ε obtained after the tth iteration t The set of all columns of the selected gamma spectrum response matrix R, r t Indicates that the tth iteration is based on the index value ε t Select the column elements of the gamma spectrum response matrix R;

[0058] Step 4.4: Solve using the least squares method make Among them, x t represents a column vector of t×1, Represents the gamma spectrum response matrix R selected after the tth iteration t Under the condition that the residual is the smallest, t Solution, that is, the characteristic peak height closest to the ideal sparse energy spectrum;

[0059] Step 4.5: According to Update residual value Update the number of iterations t = t + 1;

[0060] Step 4.6: If the updated number of iterations t≤k, repeat steps 4.2-4.5; otherwise, As the reconstructed original outgoing gamma spectrum, that is, the characteristic peak of the energy spectrum with sparsity k, the corresponding energy is Σ t The stored index value.

[0061] The present invention adopts a reconstruction algorithm to inversely solve and obtain the original emitted gamma energy spectrum. The signal represents the emitted energy spectrum of the gamma radioactive nuclide. The emitted energy spectrum can be understood as an ideal energy spectrum. The energy and total energy peak area information of the nuclide can be directly read from the energy spectrum, and the nuclide identification and total energy peak area calculation can be directly performed, and then the activity of the nuclide can be obtained to complete the activity calculation work.

[0062] In a technical solution of the present invention, a computer-readable storage medium is also provided, storing a computer program, wherein the computer program enables a computer to execute the gamma spectrum high-energy resolution reconstruction method.

[0063] In another technical solution of the present invention, an electronic device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the high-energy-resolution reconstruction method of the gamma spectrum is implemented.

[0064] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of computer storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0065] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0066] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A gamma spectrum high energy resolution reconstruction method, characterized in that: The specific steps include: Step 1, acquiring a gamma energy spectrum signal by a NaI (Tl) detector, performing energy spectrum calibration on the acquired gamma energy spectrum signal to obtain a gamma energy spectrum with energy spectrum calibration, and calculating a broadening coefficient; Step 2: According to the gamma spectrum with energy spectrum scale and the broadening coefficient, the gamma spectrum response matrix is ​​constructed in combination with the Monte Carlo method to obtain the gamma spectrum response matrix; Step 3, compressing the gamma energy spectrum signal acquired by the NaI (Tl) detector through the gamma energy spectrum response matrix, and preprocessing the gamma energy spectrum after the energy spectrum compression; Step 4: Reconstruct the preprocessed gamma energy spectrum using an inverse solution algorithm to obtain a reconstructed original emission gamma energy spectrum for nuclide analysis.

2. A gamma spectrum high energy resolution reconstruction method according to claim 1, characterized in that: Step 1: Obtain the gamma energy spectrum signal from the NaI (Tl) detector and perform spectrum calibration to obtain the discretized gamma detection energy spectrum y (n): Where n' is the channel address of the emitted gamma energy spectrum, N' is the channel address number, n is the channel address of the measured energy spectrum detected by the NaI(Tl) detector, N is the channel address number of the measured energy spectrum, x(n') is the emitted energy spectrum of the n'th energy in the gamma energy spectrum, and r(n'→n) is the unit response of the n'th energy in the gamma energy spectrum at the nth energy point in the measured energy spectrum.

3. A gamma spectrum high energy resolution reconstruction method according to claim 1, characterized in that: The specific process of step 2 is as follows: construct a detector model according to the size of the NaI(Tl) detector in the Monte Carlo model, and use the characteristic peak broadening, energy counting interval and the number of particles in the energy counting interval obtained by measuring the gamma energy spectrum with an energy spectrum scale as the input of the detector model to obtain the gamma energy spectrum response matrix.

4. A gamma spectrum high energy resolution reconstruction method according to claim 1, characterized in that: The compression process of the gamma spectrum in step 3 is: y=Rx Among them, y is the compressed gamma energy spectrum, x is the original output gamma energy spectrum to be solved, and R is the gamma energy spectrum response matrix.

5. The method for high energy resolution reconstruction of gamma spectrum according to claim 1, characterized in that: The process of preprocessing the gamma spectrum after the spectrum compression in step 3 includes: spectrum smoothing and spectrum background subtraction.

6. A gamma spectrum high energy resolution reconstruction method according to claim 5, characterized in that: Step 4 includes the following sub-steps: Step 4.1: Initialize the residual value Index Set Iteration number t = 1, original gamma spectrum sparsity k, where, is an empty set, is the gamma spectrum after preprocessing; Step 4.2: Calculate the index value Where N represents the number of columns of the gamma spectrum response matrix, j represents the index of N, and r j represents the jth column in the gamma spectrum response matrix, | <f t-1 ,r j >| indicates calculation of f t-1 With r j The inner product value of ; Step 4.3: Update the index set Σ according to the calculated index value t =Σ t-1 ∪{ε t }, R t =R t-1 ∪r t , where R t Represents the index value ε obtained after the tth iteration t The set of all columns of the selected gamma spectrum response matrix R, r t Indicates that the tth iteration is based on the index value ε t Select the column elements of the gamma spectrum response matrix R; Step 4.4: Solve using the least squares method make Among them, x t represents a column vector of t×1; Step 4.5: According to Update residual value Update the number of iterations t = t + 1; Step 4.6: If the updated number of iterations t≤k, repeat steps 4.2-4.5; otherwise, as the reconstructed raw outgoing gamma spectrum.

7. A computer-readable storage medium storing a computer program, characterized in that: The computer program enables a computer to execute the gamma spectrum high energy resolution reconstruction method as described in any one of claims 1 to 6.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for high-energy-resolution reconstruction of a gamma spectrum as described in any one of claims 1 to 6 is implemented.

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