A method and device for locating gamma photons by using a quadrature strip CdZnTe detector

Abstract

The present application relates to a kind of orthogonal strip-shaped tellurium zinc cadmium detector gamma photon positioning method and device, obtain the current signal respectively generated by the interaction of multiple gamma rays and orthogonal strip-shaped tellurium zinc cadmium detector;Multiple current signals are vectorized, and input vector set is obtained;Based on the position of multiple electrode strips in orthogonal strip-shaped tellurium zinc cadmium detector, initialization neuron network is constructed, and the number of initialization neurons is same with the number of multiple electrode strips;Multiple initialization neurons are weight vectorized, and initialization weight vector set is constructed;Based on input vector set and initialization weight vector set, through self-organizing mapping algorithm, the range of initialization weight vector set is iteratively reduced, and target weight vector is obtained, to determine the position of gamma photon.The purpose of determining the position of gamma photon based on neural network and self-organizing mapping algorithm is achieved, so as to realize the technical effects of improving the accuracy of gamma photon position and improving the efficiency of position acquisition.

Description

Technical Field

[0001] This invention relates to the field of cadmium zinc telluride detector technology, and more particularly to a method and apparatus for locating gamma photons in an orthogonal strip cadmium zinc telluride detector. Background Technology

[0002] The zinc-cadmium telluride detector is a high-performance room-temperature semiconductor nuclear radiation detector. Zinc-cadmium telluride material belongs to group II-VI compounds and has characteristics such as a large relative atomic number, a large band gap, high resistivity, and low leakage current. Furthermore, by utilizing a reasonable electrode structure, it can achieve high energy resolution and spatial resolution, and is widely used in the field of nuclear radiation imaging.

[0003] In the process of nuclear radiation imaging, gamma rays are used to irradiate a cadmium zinc telluride detector. The gamma rays interact with the cadmium zinc telluride detector to form gamma photons. The position of the gamma photons is obtained, and the final imaging result is obtained based on the position of the gamma photons.

[0004] Because the relevant technologies mostly use mathematical models to calculate the position of gamma photons, the obtained position data has deviations, resulting in inaccurate positions of gamma photons generated by the interaction of gamma rays with orthogonal strip cadmium zinc telluride detectors, and the efficiency of position acquisition is low.

[0005] The above problems urgently need to be addressed. Summary of the Invention

[0006] This invention discloses a method and apparatus for locating gamma photons in an orthogonal strip cadmium zinc telluride detector, aiming to solve the technical problems existing in the prior art.

[0007] The present invention adopts the following technical solution:

[0008] On one hand, the present invention provides a method for locating gamma photons in an orthogonal bar cadmium zinc telluride detector, comprising: acquiring current signals generated by the interaction of multiple gamma rays with the orthogonal bar cadmium zinc telluride detector; vectorizing the multiple current signals to obtain an input vector set; constructing an initialization neural network based on the positions of multiple electrode strips within the orthogonal bar cadmium zinc telluride detector, wherein the number of initialization neurons is the same as the number of multiple electrode strips; vectorizing the weights of the multiple initialization neurons to construct an initialization weight vector set; and, based on the input vector set and the initialization weight vector set, iteratively narrowing the range of the initialization weight vector set using a self-organizing map algorithm to obtain a target weight vector, thereby determining the position of the gamma photons generated by the interaction of gamma rays with the orthogonal bar cadmium zinc telluride detector.

[0009] Optionally, the step of iteratively narrowing the range of the initial weight vector set using a self-organizing map algorithm based on the input vector set and the initial weight vector set to obtain a target weight vector and determine the position of the gamma photon generated by the interaction between the gamma ray and the orthogonal strip cadmium zinc telluride detector includes: constructing an initial neighborhood range of the initial weight vector set, wherein the neighborhood range is used to indicate the region range formed by the initial weight vector set; arbitrarily sampling a first input vector in the input vector set until all input vectors in the input vector set have been sampled; iteratively updating the initial weight vector set based on the first input vector to obtain a target weight vector set, wherein the first input vector is used to indicate the currently sampled input vector; obtaining a target weight vector based on the first neighborhood range and the target weight vector set; and determining the position of the gamma photon based on the target weight vector.

[0010] Optionally, the step of arbitrarily sampling a first input vector from the input vector set until all input vectors in the input vector set have been sampled, and iteratively updating the initial weight vector set based on the first input vector to obtain the target weight vector set, includes: performing similarity matching between the first input vector and the initial weight vector set to determine a first weight vector, wherein the first weight vector is used to indicate the weight vector in the initial weight vector set with the smallest distance interval to the first input vector; updating the initial neighborhood range based on the first input vector to obtain a first neighborhood range, wherein the first neighborhood range is used to indicate the region centered on the first input vector; generating the same number of first neurons as the plurality of electrode strips within the first neighborhood range, wherein the first neurons are used to indicate the position information of the orthogonal strip cadmium zinc telluride detector within the first neighborhood range; and performing weight vectorization processing on the plurality of first neurons to construct the target weight vector set.

[0011] Optionally, updating the initial neighborhood range based on the first input vector to obtain the first neighborhood range includes: determining the minimum distance interval based on the first input vector and the first weight vector; determining the effective width of the first neighborhood range, wherein the effective width is half of the diameter of the first neighborhood range; and determining the first neighborhood range based on the effective width and the minimum distance interval.

[0012] Optionally, determining the minimum distance interval based on the first input vector and the first weight vector includes: the minimum distance interval is calculated as follows:

[0013] i(x) = arg min||X j -ω j ||

[0014] Where i(x) is the minimum distance interval, X j Let ω be the first input vector of the j-th element. j Let be the first weight vector of the j-th element, and argmin be the function to find the minimum value.

[0015] Optionally, determining the first neighborhood range based on the effective width and the minimum distance interval includes: the first neighborhood range is calculated as follows:

[0016] h (j,i(x)) =exp(-||X) j -ω j || 2 / (2σ 2 ))

[0017] Among them, h (j,i(x)) X is the first neighborhood range. j Let ω be the first input vector of the j-th element. j Let ||X| be the first weight vector of the j-th element. j -ω j || represents the distance interval, and σ represents the effective width.

[0018] Optionally, obtaining the target weight vector based on the first neighborhood range and the target weight vector set includes: the target weight vector is calculated as follows:

[0019] w j+1 =ω j +αh (j,i(x)) (X j -ω j )

[0020] Among them, w j+1 Let ω be the target weight vector. j Let h be the first weight vector of the j-th element. (j,i(x)) Let X be the first neighborhood range, α be the learning rate parameter, and X be... j Let be the first input vector of the j-th element.

[0021] According to another aspect of the present invention, a gamma photon positioning device for an orthogonal strip cadmium zinc telluride detector is also provided, comprising: an acquisition module for acquiring current signals generated by the interaction of multiple gamma rays with the orthogonal strip cadmium zinc telluride detector; an input vector module for vectorizing the multiple current signals to obtain an input vector set; a neural network module for constructing an initialization neural network based on the positions of multiple electrode strips within the orthogonal strip cadmium zinc telluride detector, wherein the number of initialization neurons is the same as the number of multiple electrode strips; a weight vector module for vectorizing the weights of the multiple initialization neurons to construct an initialization weight vector set; and a position determination module for determining the position of gamma photons generated by the interaction of gamma rays with the orthogonal strip cadmium zinc telluride detector by iteratively narrowing the range of the initialization weight vector set using a self-organizing map algorithm based on the input vector set and the initialization weight vector set to obtain a target weight vector.

[0022] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium storing a plurality of instructions adapted for loading by a processor and executing any one of the above-described orthogonal strip cadmium zinc telluride detector gamma photon localization methods.

[0023] According to another aspect of the present invention, a computer program product is also provided, comprising a computer program that, when executed by a processor, implements the steps of the orthogonal strip cadmium zinc telluride detector gamma photon localization method as described in any one of the present invention.

[0024] The technical solution adopted in this invention can achieve at least one of the following beneficial effects:

[0025] In this embodiment of the invention, current signals generated by the interaction of multiple gamma rays with an orthogonal strip cadmium zinc telluride detector are acquired. These current signals are then vectorized to obtain an input vector set. An initialization neural network is constructed based on the positions of multiple electrode strips within the orthogonal strip cadmium zinc telluride detector, wherein the number of initialization neurons is the same as the number of electrode strips. The weights of these initialization neurons are then vectorized to construct an initialization weight vector set. Based on the input vector set and the initialization weight vector set, a self-organizing mapping algorithm is used to iteratively narrow the range of the initialization weight vector set to obtain a target weight vector, thereby determining the position of the gamma photon generated by the interaction of gamma rays with the orthogonal strip cadmium zinc telluride detector. This achieves the goal of determining the position of the gamma photon based on a neural network and a self-organizing mapping algorithm, thus improving the accuracy and efficiency of gamma photon position acquisition. This solves the technical problem that related technologies often use mathematical models to calculate the position of gamma photons, resulting in biased position data and low position acquisition efficiency. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below, forming part of the present invention. The illustrative embodiments of the present invention and their descriptions explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0027] Figure 1 This is a flowchart of a gamma photon localization method for an orthogonal strip cadmium zinc telluride detector according to Embodiment 1 of the present invention;

[0028] Figure 2 This is a neural network diagram of a gamma photon localization method for an orthogonal strip cadmium zinc telluride detector in Embodiment 1 of the present invention;

[0029] Figure 3 This is a flowchart of an optional orthogonal strip cadmium zinc telluride detector gamma photon localization method in Embodiment 2 of the present invention;

[0030] Figure 4 This is a current signal position spectrum obtained by an orthogonal bar cadmium zinc telluride detector gamma photon positioning method in Embodiment 2 of the present invention;

[0031] Figure 5 This is a schematic diagram of the structure of an orthogonal strip-shaped cadmium zinc telluride detector gamma photon positioning device in Embodiment 3 of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. In the description of this invention, it should be noted that the term "or" is generally used to include the meaning of "and / or," unless otherwise expressly indicated.

[0033] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or a magnetic connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. Furthermore, in the description of this application, the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. In the description of this invention, "a plurality of" means at least two, such as two, three, or more, unless otherwise explicitly specified.

[0034] Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0035] First, to facilitate understanding of the embodiments of the present invention, some terms or nouns involved in the present invention will be explained below:

[0036] The orthogonal bar cadmium zinc telluride detector employs a design of vertically intersecting anode and cathode bars, forming a grid-like electrode structure. When X-rays or gamma rays are incident, the detector collects the charge signals on the orthogonal electrodes and, combined with waveform analysis techniques, accurately calculates the three-dimensional coordinates (including depth information) of the ray's location.

[0037] To address the problems existing in the prior art, this application provides a method and apparatus for locating gamma photons using an orthogonal strip cadmium zinc telluride detector.

[0038] Example 1

[0039] This embodiment provides a method for locating gamma photons using an orthogonal strip cadmium zinc telluride detector, such as... Figure 1 As shown, Figure 1 This is a flowchart of a gamma photon localization method for an orthogonal strip cadmium zinc telluride detector according to Embodiment 1 of the present invention. The method includes:

[0040] Step S102: Acquire the current signals generated by the interaction of multiple gamma rays with the orthogonal strip cadmium zinc telluride detector;

[0041] Optionally, since the interaction between gamma rays and the orthogonal strip cadmium zinc telluride detector will generate electron-hole pairs (gamma photons) and drift directionally under the action of the electric field, the anode and cathode (electrode strips) of the orthogonal strip cadmium zinc telluride detector will generate current signals. By collecting these current signals, the dataset x required to detect the position of gamma photons can be obtained.

[0042] Optionally, when gamma rays interact with the orthogonal cadmium zinc telluride (CZN) detector, physical processes such as Compton scattering and the photoelectric effect will occur, potentially creating one or more interaction sites within the CZN detector. For multiple interaction sites (i.e., multiple scattering events), different current signals will be obtained on the multiple anode and cathode bars of the CZN detector; however, in a single interaction event, the CZN detector generates a current signal only on the anode and cathode bars corresponding to the interaction site, where the magnitude of the current signal is related to the ray deposition energy. In a single interaction event, the collected current signal is used as data within dataset x. When multiple interaction events occur, the collected current signals are divided into multiple single interaction event data, and dataset x is collected according to the single interaction event method.

[0043] Optionally, the acquired dataset x is preprocessed, including energy filtering and data removal exceeding a threshold, to obtain the full-energy peak event dataset x. n Where n is the number of data points after filtering the dataset. Full-energy peak event data includes information such as energy, time, and location. Its core indicators include: Peak position: corresponding to the energy of the incident particle (e.g., ...). 137 Cs 0.662 MeV peak), peak area: reflects the number of events and is proportional to the activity of the radioactive source, energy resolution: peak width / peak position (e.g., 0.39% for CZT detectors), peak shape parameters: such as full width at half maximum (FWHM) and symmetry, used to evaluate detector performance.

[0044] Step S104: Vectorize multiple current signals to obtain an input vector set;

[0045] Optionally, the preprocessed dataset x becomes the all-energy peak event dataset x. n The data in the dataset is still a current signal, but the full-energy peak event dataset x n The current signal in the dataset is used to remove current signals that exceed a threshold. At this point, the full-energy peak event dataset x... n The current signal in the input vector is vectorized to obtain the input vector set X. nIt should be noted that a vector includes both direction and magnitude; therefore, the input vector set X... n The input vectors in the input vectors all include the magnitude of the current signal and the direction of current movement.

[0046] Step S106: Construct an initial neural network based on the positions of multiple electrode strips within the orthogonal strip cadmium zinc telluride detector, wherein the number of initial neurons is the same as the number of multiple electrode strips;

[0047] Optionally, to obtain the location of gamma photons, the location of the current signal needs to be matched with the location of the electrode strips. Since the location of the current signal is unknown, and it is generated on the electrode strips, the location of the current signal can be determined based on the location of the electrode strips. When gamma rays act on different depths of the orthogonal strip cadmium telluride detector, the signals acquired by the detector at the anode and cathode (electrode strips) still differ. Therefore, it is necessary to utilize the characteristics of the detection signals acquired at different locations and construct a self-organizing map algorithm (SOM algorithm) to calculate the interaction location of the rays. Specifically, by setting the number of initial neurons to be the same as the number of electrode strips in the space of the orthogonal strip cadmium telluride detector, the weight values ​​of the initial neurons are adjusted and the topology is processed using single interaction event data. This allows the topology of the initial neurons to directly output the probability distribution map of gamma ray interactions within the space of the orthogonal strip cadmium telluride detector, thus visually displaying the interaction locations of the gamma rays.

[0048] Optional, such as Figure 2 As shown, Figure 2 This is a neural network diagram of a gamma photon localization method for an orthogonal strip cadmium zinc telluride detector according to Embodiment 1 of the present invention. Figure 2 This diagram illustrates a neural network for SOM algorithm position calculation on a plane. The diagram shows 16 initialized neurons, each represented by a hollow circle, and utilizes the input vector set X. n The SOM algorithm is used to calculate the topology and weights of these neurons, resulting in a 4×4 array of neuron topologies. By using the constructed SOM algorithm framework and inputting current signal data collected by different orthogonal strip cadmium zinc telluride detectors, the location distribution map of interactions can be visually displayed. Furthermore, for multiple interaction events, multiple high-probability distribution points can appear in the location distribution map.

[0049] Step S108: Perform weight vectorization processing on multiple initialized neurons to construct an initialized weight vector set;

[0050] Optionally, in the constructed SOM algorithm framework, the weights of each initial neuron need to be vectorized to obtain an initial weight vector set. Since the probability of each electrode strip generating a current signal is equal in the initial stage, the weight values ​​of the initial neurons corresponding to each electrode strip are all equal. To calculate the interaction position between the orthogonal strip cadmium zinc telluride detector and gamma rays, the initial neurons are assigned to each electrode strip in the neuron topology of the SOM algorithm. Furthermore, the initial weight vector set ω on each electrode strip is... L Random selection is performed, where L is the number of electrode strips, through the input vector set X. n The number of times the current signal is generated on each electrode strip and the position of the electrode strip corresponding to the current signal will initialize the initial weight vector set ω corresponding to the neuron. L The process involves iterative updates, with the weight vector on each electrode bar changing. Ultimately, the neuron with the largest weight value is found, and the position of the gamma photon is determined based on the determined neuron.

[0051] Step S110: Based on the input vector set and the initial weight vector set, the range of the initial weight vector set is iteratively narrowed through the self-organizing mapping algorithm to obtain the target weight vector, and the position of the gamma photon generated by the interaction between the gamma ray and the orthogonal strip cadmium zinc telluride detector is determined.

[0052] Optionally, the above obtains the input vector set X corresponding to the current signal generated by the interaction between gamma rays and the orthogonal strip cadmium zinc telluride detector. n And the initial weight vector set ω corresponding to the positions of the electrode strips on the orthogonal strip cadmium zinc telluride detector. L , input vector set X n With the initial weight vector set ω L Perform matching to determine the input vector set X n The current signal and the initial weight vector set ω in L The closest weight vector is used to pinpoint the initial location range of the current signal (i.e., the gamma photon), based on the input vector set X. n By performing multiple matching operations on multiple input vectors, the initial weight vector set ω can be gradually reduced. L The range, that is, the location range is gradually narrowed, within the input vector set X. n After iteratively matching all input vectors, the weight vector set ω is initialized. L The range has been narrowed down sufficiently in the last input vector set X. n With the initial weight vector set ω L During the matching process, a target weight vector is found in the last iteration. The neuron is determined by the target weight vector, and then the precise location of the gamma photon is determined.

[0053] In some preferred embodiments, based on the input vector set and the initial weight vector set, the range of the initial weight vector set is iteratively narrowed using a self-organizing map algorithm to obtain the target weight vector, thereby determining the position of the gamma photon generated by the interaction between the gamma ray and the orthogonal strip cadmium zinc telluride detector. This includes: constructing an initial neighborhood range of the initial weight vector set, wherein the neighborhood range is used to indicate the region range formed by the initial weight vector set; arbitrarily sampling a first input vector in the input vector set until all input vectors in the input vector set have been sampled; iteratively updating the initial weight vector set based on the first input vector to obtain the target weight vector set, wherein the first input vector is used to indicate the currently sampled input vector; obtaining the target weight vector based on the first neighborhood range and the target weight vector set; and determining the position of the gamma photon based on the target weight vector.

[0054] Optionally, the initial weight vector set is a vector with direction and magnitude, and the initial weight vector set represents the position. When the initial weight vector set is placed in the coordinate system, the entire initial weight vector set will form an initial neighborhood range. Before iteration, the initial neighborhood range is the area of ​​the entire orthogonal strip cadmium zinc telluride detector.

[0055] Optionally, the first input vector, randomly sampled from the input vector set, is matched with the initial weight vector set. This involves matching the current signal to the electrode strip position of the orthogonal strip cadmium zinc telluride detector. A new first neighborhood is defined with this position as the center and a semicircle with a radius set according to experimental accuracy requirements. The first neuron is then updated within this first neighborhood, resulting in a new initial weight vector. By inputting the second first input vector, the iteration continues within the updated first neighborhood and the new initial weight vector, updating the position until the last first input vector is reached, thus completing the iteration. The final input position is the position of the gamma photon.

[0056] Optionally, the input vector set X can be iterated through multiple inputs. n Domain function h (j,i(x)) The weight vector of the neuron will gradually decrease, the topological structure of the neuron will gradually stabilize, and the change of the weight vector of the neuron will gradually decrease. Finally, the position of the orthogonal strip cadmium zinc telluride detector corresponding to the weight vector obtained in the last iteration will be determined, which is the position of the gamma photon.

[0057] In some preferred embodiments, a first input vector is arbitrarily sampled from the input vector set until all input vectors in the input vector set have been sampled. The initial weight vector set is then iteratively updated based on the first input vector to obtain the target weight vector set. This includes: performing similarity matching between the first input vector and the initial weight vector set to determine a first weight vector, wherein the first weight vector indicates the weight vector in the initial weight vector set with the smallest distance interval to the first input vector; updating the initial neighborhood range based on the first input vector to obtain a first neighborhood range, wherein the first neighborhood range indicates the region centered on the first input vector; generating the same number of first neurons as the multiple electrode strips within the first neighborhood range, wherein the first neurons indicate the positional information of the orthogonal strip cadmium zinc telluride detector within the first neighborhood range; and performing weight vectorization processing on the multiple first neurons to construct the target weight vector set.

[0058] Optionally, similarity matching is performed based on the first input vector and the initialized weight vector set. That is, the first input vector is compared with multiple weight vectors in the initialized weight vector set to determine a weight vector that is closest to the first input vector. In other words, the electrode strip position that is closest to the current signal is determined, and the weight vector that is closest to the first input vector is defined as the first weight vector.

[0059] Optionally, the first weight vector is equivalent to a location point within the initial neighborhood, which is also the preliminary location of the gamma photon. However, since the first input vector is matched only once, the location of the gamma photon may be deviated. Therefore, it is necessary to reduce the range of the initial neighborhood to determine a more accurate location of the gamma photon. Specifically, the initial neighborhood is defined by taking the first weight vector as a circle and using a semicircle set by the experimental accuracy requirements as the radius. It should be noted that the radius is set manually according to the experimental accuracy requirements. The first neuron is then updated within the first neighborhood, resulting in a new set of weight vectors, i.e., the target weight vector set.

[0060] Optionally, by constructing the complete SOM framework, the full-energy peak event dataset x can be utilized. n The weights of the initialized neurons are adjusted and optimized. This is done to find the input vector set X. n With the initial weight vector set ω L The optimal match is determined by calculating the distance (usually Euclidean distance) between each data point in the input vector set and the corresponding weight vector of each neuron in the orthogonal striped cadmium zinc telluride detector electrode grid distribution. The neuron with the smallest distance represents the optimal matching unit (the first weight vector). The formula for calculating the minimum interval distance between the first input vector and the initialized weight vector set is as follows:

[0061] i(x) = argmin||X j -ω j ||

[0062] Where i(x) is the minimum distance interval, X j Let ω be the first input vector of the j-th element. j Let be the first weight vector of the j-th input vector, and let argmin be the minimum value of the function. The first neuron i that satisfies the minimum distance is called the best matching or the winning neuron in the competition for the first input vector.

[0063] In some preferred embodiments, updating the initial neighborhood range based on the first input vector to obtain the first neighborhood range includes: determining the minimum distance interval based on the first input vector and the first weight vector; determining the effective width of the first neighborhood range, wherein the effective width is half of the diameter of the first neighborhood range; and determining the first neighborhood range based on the effective width and the minimum distance interval.

[0064] In some preferred embodiments, determining the minimum distance interval based on the first input vector and the first weight vector includes: the minimum distance interval is calculated as follows:

[0065] i(x) = arg min||X j -ω j ||

[0066] Where i(x) is the minimum distance interval, X j Let ω be the first input vector of the j-th element. j Let be the first weight vector of the j-th element, and argmin be the function to find the minimum value.

[0067] In some preferred embodiments, the first neighborhood range is determined based on the effective width and the minimum distance interval, including: the first neighborhood range is calculated as follows:

[0068] h (j,i(x)) =exp(-||X) j -ω j || 2 / (2σ 2 ))

[0069] Among them, h (j,i(x)) X is the first neighborhood range. j Let ω be the first input vector of the j-th element. j Let ||X| be the first weight vector of the j-th element. j -ω j || represents the distance interval, and σ represents the effective width. It should be noted that the minimum distance interval can be selected during the calculation process.

[0070] Optionally, neurons surrounding the winning first neuron are designated as excitatory neurons, and new neurons are generated around these excitatory neurons. Simultaneously, the range of these excitatory neurons is reorganized into the first neighborhood range. When calculating the distance between the first input vector and the weight vector set multiple times, the narrowed range of the neuron also exhibits a small distance value. To appropriately adjust the topology of the neuron, a new first neighborhood range h can be defined. (j,i(x)) The first neighborhood range h (j,i(x)) It is symmetric about the winning first neuron, and its amplitude value decreases monotonically with increasing lateral distance.

[0071] In some preferred embodiments, the target weight vector is obtained based on the first neighborhood range, the first weight vector, and the target weight vector set, including: the target weight vector is calculated as follows:

[0072] w j+1 =ω j +αh (j,i(x)) (X j -ω j )

[0073] Among them, w j+1 Let ω be the target weight vector. j Let h be the first weight vector of the j-th element. (j,i(x)) Let X be the first neighborhood range, α be the learning rate parameter, and X be... j Let be the first input vector of the j-th element.

[0074] Optionally, the weight vector ω for each neuron can be optimized by adjusting the weights using the following formula:

[0075] w j+1 =ω j +αh (j,i(x)) (X j -ω j )

[0076] w j+1 The target weight vector is the weight vector after adjustment, ω j The first weight vector before weight adjustment, where α is the learning rate parameter, and h... (j,i(x)) The first neighborhood range around the winning neuron.

[0077] Through the above steps S102 to S110, the goal of determining the position of gamma photons based on neural networks and self-organizing mapping algorithms is achieved, thereby improving the accuracy of gamma photon position and the efficiency of position acquisition. This solves the technical problem that the position data obtained by using mathematical models to calculate the position of gamma photons in related technologies is biased and the position acquisition efficiency is low.

[0078] Example 2

[0079] Based on the above embodiments and optional embodiments, the present invention also proposes an optional implementation method. Figure 3 This is a flowchart of an optional orthogonal strip cadmium zinc telluride detector gamma photon localization method according to Embodiment 2 of the present invention, as shown below. Figure 3 As shown, the method includes:

[0080] To calculate the position of gamma photons within a cadmium zinc telluride crystal (orthogonal bar cadmium zinc telluride detector), an initial weight vector is set on each electrode bar of the cadmium zinc telluride crystal. Figure 2 (represented by the circle in the middle), by multiplying the input vector and the initial weight vector, the probability of gamma rays interacting on each electrode strip (i.e., the iteratively updated weight value) is calculated. Through the probability distribution (weight value distribution) corresponding to the electrode strip, the position within the cadmium zinc telluride crystal is calculated.

[0081] Step S1, Initialization: Initialize the weight vector set ω on each electrode bar. L Random selection is performed, where L is the number of electrode strips, and ω L The initial weight vectors within each vector are all different.

[0082] Step S2, Sampling: Select training samples from the input vector set with a 70% probability to construct and train the self-organizing map algorithm, and select validation samples from the input vector set with a 30% probability to obtain the final self-organizing map algorithm; randomly select an input vector from the input vector set as the first input vector.

[0083] Step S3, Similarity Matching: Use the minimum distance criterion to find the best matching (winning) electrode strip, as well as the first neuron and first weight vector corresponding to the electrode strip. The minimum distance criterion is:

[0084] i(x) = argmin||X j -ω j ||

[0085] Where i(x) is the minimum distance interval, X j Let ω be the first input vector of the j-th element. j Let be the first weight vector of the j-th element.

[0086] Step S4, Pixel weight vector update: Adjust the first weight vector using the update formula to obtain the final target weight vector.

[0087] w j+1 =ω j +αh (j,i(x)) (X j -ω j )

[0088] h (j,i(x)) =exp(-||X) j -ω j || 2 / (2σ 2 ))

[0089] Among them, w j+1 Let ω be the target weight vector. j Let h be the first weight vector of the j-th element. (j,i(x)) Let X be the first neighborhood range, α be the learning rate parameter, and X be... j h is the first input vector for the j-th element. (j,i(x)) X is the first neighborhood range. j Let ω be the first input vector of the j-th element. j Let ||X| be the first weight vector of the j-th element. j -ω j || represents the minimum distance interval, and σ represents the effective width.

[0090] To achieve the best results, h (j,i(x)) The two parameters, α and β, change dynamically during the calculation.

[0091] Step S5, Iteration: Continue the loop from step S2 to step S4 for multiple iterations until the first neighborhood range of the feature map observation has been reduced to a diameter of less than 0.01 mm, or all input vectors in the input vector set have completed iteration, to obtain the final target weight vector. Based on the target weight vector, the neuron is derived, and based on the neuron, the position information is determined to obtain the precise position of the gamma photon.

[0092] Based on the self-organizing map algorithm, the waveform acquisition experimental data of the orthogonal bar cadmium zinc telluride (Na-22) radioactive source were used for training to obtain the weight value of each position on the xy plane, and the two-dimensional position spectrum of the cathode-anode signal was plotted as follows: Figure 4 As shown. Figure 4 This is a current signal position spectrum obtained by a gamma photon localization method for an orthogonal bar cadmium telluride detector according to Embodiment 2 of the present invention. Figure 4 The data shows that the weight potential at the center of each electrode strip is much higher than that at nearby positions, and the overall weight distribution is relatively uniform, consistent with the signal position distribution calculated by the signal weighting method formula.

[0093] In summary, the distribution maps (x, y, z) of the cathode-anode two-dimensional position spectrum, cathode-depth two-dimensional position spectrum, and anode-depth two-dimensional position spectrum obtained from the Na-22 radioactive source test waveform acquisition of the 1.0 mm orthogonal bar cadmium telluride (CdT) detector show good agreement with the simulation results of the detector's electric field and weighted potential. The position signal weighted method calculation value of the orthogonal bar CdT detector maintains good consistency with the position value predicted by the self-organizing mapping algorithm based on the CdT detector experimental data, verifying the correctness of the proposed CdT detector position calculation method.

[0094] Through the above steps S1 to S5, software technology can be used to promote the acquisition of gamma ray position information in the position-sensitive orthogonal strip cadmium zinc telluride detector, reducing the cost of improving position resolution, improving the efficiency of position acquisition, and enabling the application of neural network algorithms to further refine the position information of gamma rays in the orthogonal strip position-sensitive cadmium zinc telluride detector.

[0095] Example 3

[0096] According to an embodiment of the present invention, an embodiment of an apparatus for implementing the above-described orthogonal strip cadmium zinc telluride detector gamma photon localization is also provided. Figure 5 This is a schematic diagram of the structure of an orthogonal strip-shaped cadmium zinc telluride detector gamma photon positioning device according to Embodiment 3 of the present invention, as shown below. Figure 5 As shown, the above-mentioned positioning device includes: an acquisition module 301, an input vector module 302, a neural network module 303, a weight vector module 304, and a position determination module 305, wherein:

[0097] The acquisition module 301 is used to acquire the current signals generated by the interaction of multiple gamma rays with the orthogonal strip cadmium zinc telluride detector;

[0098] The input vector module 302, connected to the acquisition module 301, is used to perform vectorization processing on multiple current signals to obtain an input vector set.

[0099] The neural network module 303, connected to the input vector module 302, is used to construct an initial neural network based on the positions of multiple electrode strips within the orthogonal strip cadmium zinc telluride detector, wherein the number of initial neurons is the same as the number of multiple electrode strips;

[0100] The weight vector module 304 is connected to the neural network module 303 and is used to perform weight vectorization processing on multiple initial neurons to construct an initial weight vector set.

[0101] The position determination module 305, connected to the weight vector module 304, is used to determine the position of the gamma photons generated by the interaction between the gamma rays and the orthogonal strip cadmium zinc telluride detector by iteratively narrowing the range of the initial weight vector set based on the input vector set and the initial weight vector set through a self-organizing mapping algorithm.

[0102] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0103] It should be noted that the acquisition module 301, input vector module 302, neural network module 303, weight vector module 304, and position determination module 305 mentioned above correspond to steps S102 to S110 in the embodiments. The instances and application scenarios implemented by the above modules and their corresponding steps are the same, but they are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in a computer terminal.

[0104] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0105] The above-mentioned orthogonal bar zinc cadmium telluride detector gamma photon positioning device may also include a processor and a memory. The above-mentioned acquisition module 301, input vector module 302, neural network module 303, weight vector module 304 and position determination module 305 are all stored in the memory as program modules, and the processor executes the above-mentioned program modules stored in the memory to realize the corresponding functions.

[0106] The processor contains a core that retrieves the corresponding program modules from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0107] According to an embodiment of this application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program is running, it controls the device where the non-volatile storage medium is located to execute any of the above-mentioned orthogonal strip cadmium zinc telluride detector gamma photon localization methods.

[0108] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals, and the non-volatile storage medium includes stored programs.

[0109] Optionally, during program execution, the device containing the non-volatile storage medium performs the following functions: acquiring current signals generated by the interaction of multiple gamma rays with the orthogonal strip cadmium zinc telluride detector; vectorizing the multiple current signals to obtain an input vector set; constructing an initialization neural network based on the positions of multiple electrode strips within the orthogonal strip cadmium zinc telluride detector, wherein the number of initialization neurons is the same as the number of electrode strips; vectorizing the weights of the multiple initialization neurons to construct an initialization weight vector set; and using a self-organizing mapping algorithm to iteratively narrow the range of the initialization weight vector set based on the input vector set and the initialization weight vector set, obtaining the target weight vector, and determining the position of the gamma photons generated by the interaction of gamma rays with the orthogonal strip cadmium zinc telluride detector.

[0110] According to an embodiment of this application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any of the above-described orthogonal strip cadmium zinc telluride detector gamma photon localization methods.

[0111] According to an embodiment of this application, an embodiment of a computer program product is also provided. Optionally, in this embodiment, the computer program product includes a computer program that, when executed by a processor, implements the steps of any of the above-described orthogonal strip cadmium zinc telluride detector gamma photon localization methods.

[0112] Optionally, when the aforementioned computer program product is executed on a data processing device, it is suitable to execute an initialization program with the following steps: acquiring current signals generated by the interaction of multiple gamma rays with an orthogonal strip cadmium zinc telluride detector; vectorizing the multiple current signals to obtain an input vector set; constructing an initialization neural network based on the positions of multiple electrode strips within the orthogonal strip cadmium zinc telluride detector, wherein the number of initialization neurons is the same as the number of multiple electrode strips; vectorizing the weights of the multiple initialization neurons to construct an initialization weight vector set; and, based on the input vector set and the initialization weight vector set, iteratively narrowing the range of the initialization weight vector set using a self-organizing mapping algorithm to obtain a target weight vector, thereby determining the position of the gamma photons generated by the interaction of gamma rays with the orthogonal strip cadmium zinc telluride detector.

[0113] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: acquiring current signals generated by the interaction of multiple gamma rays with an orthogonal strip-shaped cadmium zinc telluride detector; vectorizing the multiple current signals to obtain an input vector set; constructing an initialization neural network based on the positions of multiple electrode strips within the orthogonal strip-shaped cadmium zinc telluride detector, wherein the number of initialization neurons is the same as the number of electrode strips; vectorizing the weights of the multiple initialization neurons to construct an initialization weight vector set; and iteratively narrowing the range of the initialization weight vector set using a self-organizing map algorithm based on the input vector set and the initialization weight vector set to obtain a target weight vector, thereby determining the position of the gamma photons generated by the interaction of gamma rays with the orthogonal strip-shaped cadmium zinc telluride detector.

[0114] The order of the above embodiments of the present invention is merely for description and does not represent the superiority or inferiority of the embodiments.

[0115] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of modules described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between modules, and may be electrical or other forms.

[0117] The modules described above as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0118] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0119] If the aforementioned integrated modules are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0120] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for locating gamma photons using an orthogonal strip-shaped cadmium zinc telluride detector, characterized in that, include: Acquire the current signals generated by the interaction of multiple gamma rays with an orthogonal strip cadmium zinc telluride detector; The multiple current signals are vectorized to obtain an input vector set; An initialization neural network is constructed based on the positions of multiple electrode strips within the orthogonal strip cadmium zinc telluride detector, wherein the number of initialization neurons is the same as the number of multiple electrode strips; The initialization neurons are processed by weight vectorization to construct an initialization weight vector set; Based on the input vector set and the initial weight vector set, the range of the initial weight vector set is iteratively narrowed through a self-organizing mapping algorithm to obtain the target weight vector, thereby determining the position of the gamma photon generated by the interaction between the gamma ray and the orthogonal strip cadmium zinc telluride detector; An initial neighborhood range is constructed for the initial weight vector set, wherein the neighborhood range indicates the region range formed by the initial weight vector set; a first input vector is arbitrarily sampled from the input vector set until all input vectors in the input vector set have been sampled; the initial weight vector set is iteratively updated based on the first input vector to obtain a target weight vector set, wherein the first input vector indicates the currently sampled input vector; a target weight vector is obtained based on the first neighborhood range and the target weight vector set, wherein the first neighborhood range is obtained by updating the initial neighborhood range based on the first input vector, and the first neighborhood range indicates the region centered on the first input vector; the position of the gamma photon is determined based on the target weight vector.

2. The gamma photon localization method for an orthogonal strip cadmium zinc telluride detector according to claim 1, characterized in that, The process involves arbitrarily sampling a first input vector from the input vector set until all input vectors in the input vector set have been sampled, and then iteratively updating the initial weight vector set based on the first input vector to obtain the target weight vector set, including: The first input vector is matched with the initial weight vector set to determine the first weight vector, wherein the first weight vector is used to indicate the weight vector in the initial weight vector set that has the smallest distance interval with the first input vector; Based on the first input vector, the initial neighborhood range is updated to obtain a first neighborhood range, wherein the first neighborhood range is used to indicate the region centered on the first input vector; Within the first neighborhood, the same number of first neurons as the plurality of electrode strips are generated, wherein the first neurons are used to indicate the positional information of the orthogonal strip cadmium zinc telluride detector within the first neighborhood; The weights of multiple first neurons are vectorized to construct a target weight vector set.

3. The gamma photon localization method for an orthogonal strip cadmium zinc telluride detector according to claim 2, characterized in that, The step of updating the initial neighborhood range based on the first input vector to obtain the first neighborhood range includes: Based on the first input vector and the first weight vector, determine the minimum distance interval; Determine the effective width of the first neighborhood range, wherein the effective width is half the diameter of the first neighborhood range; The first neighborhood range is determined based on the effective width and the minimum distance interval.

4. The gamma photon localization method for an orthogonal strip cadmium zinc telluride detector according to claim 3, characterized in that, Determining the minimum distance interval based on the first input vector and the first weight vector includes: The minimum distance interval is calculated as follows: in, For the minimum distance interval, Let j be the first input vector. Let j be the first weight vector. To find the minimum value of the function.

5. The gamma photon localization method for an orthogonal strip cadmium zinc telluride detector according to claim 4, characterized in that, Determining the first neighborhood range based on the effective width and the minimum distance interval includes: The first neighborhood range is calculated as follows: ) in, The first neighborhood range, Let j be the first input vector. Let j be the first weight vector. For distance interval, This is the effective width.

6. The gamma photon localization method for an orthogonal strip cadmium zinc telluride detector according to claim 5, characterized in that, The step of obtaining the target weight vector based on the first neighborhood range and the target weight vector set includes: The target weight vector is calculated as follows: in, Let be the target weight vector. Let j be the first weight vector. The first neighborhood range, The learning rate parameter, Let be the first input vector of the j-th element.

7. A gamma photon positioning device for an orthogonal strip-shaped cadmium zinc telluride detector, characterized in that, include: The acquisition module is used to acquire the current signals generated by the interaction of multiple gamma rays with the orthogonal strip cadmium zinc telluride detector; The input vector module is used to vectorize multiple current signals to obtain an input vector set. A neural network module is used to construct an initial neural network based on the positions of multiple electrode strips within the orthogonal strip cadmium zinc telluride detector, wherein the number of initial neurons is the same as the number of multiple electrode strips; The weight vector module is used to perform weight vectorization processing on multiple initial neurons to construct an initial weight vector set; The position determination module is used to determine the position of the gamma photons generated by the interaction between gamma rays and the orthogonal strip cadmium zinc telluride detector by iteratively narrowing the range of the initial weight vector set based on the input vector set and the initial weight vector set through a self-organizing mapping algorithm, thereby obtaining the target weight vector. The location determination module is configured to: construct an initial neighborhood range for the initial weight vector set, wherein the neighborhood range indicates the region formed by the initial weight vector set; arbitrarily sample a first input vector from the input vector set until all input vectors in the input vector set have been sampled; iteratively update the initial weight vector set based on the first input vector to obtain a target weight vector set, wherein the first input vector indicates the currently sampled input vector; obtain a target weight vector based on the first neighborhood range and the target weight vector set, wherein the first neighborhood range is obtained by updating the initial neighborhood range based on the first input vector, and the first neighborhood range indicates the region centered on the first input vector; and determine the location of the gamma photon based on the target weight vector.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions, which are adapted to be loaded by a processor and executed by a method for locating gamma photons in an orthogonal strip cadmium zinc telluride detector as described in any one of claims 1 to 6.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the gamma photon localization method for an orthogonal bar cadmium zinc telluride detector as described in any one of claims 1 to 6.

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