Cosmic ray muon scattering imaging method, system and device

Abstract

The application discloses a cosmic ray muon scattering imaging method, system and device, relates to the field of ray imaging, and comprises the following steps: acquiring direction information of muons in a region to be imaged; calculating a three-dimensional most probable scattering track of the muons in the region to be imaged based on Bayesian inference and a particle multiple Coulomb scattering distribution function according to the direction information of the muons in the region to be imaged; and reconstructing a scattering density image in the region to be imaged according to the direction information of the muons in the region to be imaged and the three-dimensional most probable scattering track. The application can effectively reconstruct a scattering point by calculating the three-dimensional most probable scattering track, improves the utilization efficiency of muon events, avoids the scattering point from being separated from the region to be imaged, and makes the three-dimensional most probable scattering track more consistent with an actual scattering track, thereby improving the quality of the scattering density image.

Description

Technical Field

[0001] The present application relates to the field of ray imaging, and in particular to a cosmic ray muon scattering imaging method, system and device. Background Art

[0002] The application of nuclear energy and nuclear technology, as important achievements of modern science and technology, has driven socioeconomic development and benefited humanity, but it also carries significant safety risks. Radioactive materials, mostly containing high-atomic-number fissile nuclides such as uranium and plutonium, are often transported enclosed in shielding layers such as lead and steel. Conventional detection techniques such as X-rays, neutrons, and protons are commonly used in large container inspection systems. However, due to limited penetration and radiation hazards, they are difficult to effectively identify nuclear materials within the shielding layers. This dilemma has given rise to a new passive detection technology: cosmic ray muon scattering imaging.

[0003] Cosmic-ray muon scattering imaging leverages the unique advantages of natural muons: their exceptional penetrating power, capable of penetrating tens of meters of rock, their passive detection capabilities, and their sensitivity to artificial radiation exposure. Their sensitivity to scattering angles and lateral displacements for high-atomic-number materials provides a physical basis for material identification. Muons undergo multiple Coulomb scattering as they pass through matter, resulting in an angular deflection (Δθ) and lateral displacement (Δx) between the outgoing and incoming directions. By constructing an upstream and downstream track detection system, measuring the incoming and outgoing muon trajectories and calculating Δθ and Δx, combined with a scattering density inversion algorithm, the internal structure of the target can be reconstructed and high-atomic-number materials identified.

[0004] In the research and development of cosmic ray muon scattering imaging technology, many technical challenges are still faced. The quality of image reconstruction is limited by the position resolution and area of ​​the scattering system. Therefore, it is necessary to establish a high-resolution, large-area muon scattering imaging system. Traditional image reconstruction algorithms are difficult to efficiently utilize scattering information, and the reconstructed image accuracy is not high. Summary of the Invention

[0005] The purpose of this application is to provide a cosmic ray muon scattering imaging method, system and equipment, which can improve the quality of scattering density images.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a cosmic ray muon scattering imaging method, comprising:

[0008] Obtaining the direction information of the muon in the area to be imaged;

[0009] Based on the direction information of the muon in the area to be imaged, Bayesian inference and the particle multi-Coulomb scattering distribution function, the most probable three-dimensional scattering trajectory of the muon in the area to be imaged is calculated;

[0010] The scattering density image in the area to be imaged is reconstructed based on the direction information of the muons in the area to be imaged and the three-dimensional scattering most probable track.

[0011] In a second aspect, the present application provides a cosmic ray muon scattering imaging system, comprising:

[0012] A muon direction acquisition module is used to obtain the direction information of muons in the area to be imaged;

[0013] The scattering track estimation module is used to calculate the three-dimensional most probable scattering track of muons in the area to be imaged based on the direction information of muons in the area to be imaged, Bayesian inference and particle multi-Coulomb scattering distribution function;

[0014] The scattering density reconstruction module is used to reconstruct the scattering density image in the area to be imaged based on the direction information of the muons in the area to be imaged and the three-dimensional scattering most probable track.

[0015] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned cosmic ray muon scattering imaging method.

[0016] According to the specific embodiments provided in this application, this application has the following technical effects:

[0017] The present application provides a cosmic ray muon scattering imaging method, system, and device. Based on obtaining directional information of muons in a region to be imaged, the method calculates the three-dimensional most probable scattering track of muons in the region to be imaged based on Bayesian inference and the particle multi-Coulomb scattering distribution function. This method effectively reconstructs scattering points, improves the utilization efficiency of muon events, and avoids the problem of scattering points escaping from the region to be imaged. Furthermore, based on the directional information of muons in the region to be imaged and the three-dimensional most probable scattering track, a scattering density image in the region to be imaged is reconstructed. This method avoids track linearization processing, more closely matches the actual scattering track, and thereby improves the quality of the scattering density image. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 Schematic diagram of a muon scattering imaging execution device in one embodiment of the present application.

[0020] Figure 2This is an overall flow chart of a cosmic ray muon scattering imaging method provided in one embodiment of the present application.

[0021] Figure 3 A detailed flow chart of a cosmic ray muon scattering imaging method provided in one embodiment of the present application.

[0022] Figure 4 Schematic diagram of the trajectory of muons passing through the area to be imaged in one embodiment of the present application.

[0023] Figure 5 A schematic diagram of the functional modules of a cosmic ray muon scattering imaging system provided in one embodiment of the present application.

[0024] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0026] Among the current mainstream scattering imaging reconstruction algorithms, the Point of Closest Approach (PoCA) algorithm simplifies muon multiple scattering into a single scattering event for approximation. The scattering point is selected as the geometric intersection of the incident and outgoing tracks (when they are coplanar) or the midpoint of a common perpendicular (when they are non-coplanar), and the scattering density is calculated based on the variance of the scattering angle. When the muon tracks are non-coplanar, the reconstructed scattering points of the PoCA algorithm tend to deviate significantly from the imaging region. Furthermore, the use of a broken-line trajectory to calculate the scattering density ignores the true scattering curvature, resulting in low imaging resolution. While the Maximum Likelihood Scattering and Displacement (MLSD) algorithm improves density inversion accuracy through Monte Carlo iteration of the statistical scattering parameter distribution, it still relies on the PoCA broken-line trajectory as the initial condition, resulting in artifact blurring and error accumulation.

[0027] Furthermore, correlation scattering imaging reconstruction algorithms all require information about the muon tracks within an object, but using only simple straight or broken line tracks has limitations. A reasonable estimate of the muon tracks within an object can address these shortcomings and improve image reconstruction quality.

[0028] This application addresses the problem that existing muon imaging algorithms use straight or broken line tracks to approximate the scattering tracks of muons in the area to be imaged to calculate the scattering density for scattering imaging, resulting in low image reconstruction accuracy. A cosmic ray muon scattering imaging method based on Bayesian inference is proposed. According to the physical laws of multiple Coulomb scattering, this method uses prior knowledge such as the muon incident track and the exit track to establish a track probability distribution function based on Bayesian inference, and gives the three-dimensional most probable scattering tracks of muons incident from different directions in the area to be imaged, thereby improving the quality of image reconstruction.

[0029] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] In an exemplary embodiment, a cosmic ray muon scattering imaging method is provided, wherein Figure 1 The muon scattering imaging execution device shown in the figure performs a specific application example. First, a scattering imaging experimental platform is constructed. The scattering imaging experimental platform consists of an upstream track detection module, a downstream track detection module, and an imaging area between them. The track detection modules (including the upstream and downstream track detection modules) are used to collect muon impact position data, and the imaging area is used to place the object to be imaged. The scattering imaging experimental platform transmits the muon impact position data to a computer device, which then executes the muon scattering imaging algorithm based on the muon impact position data collected by the track detection modules.

[0031] like Figure 2 and Figure 3 As shown, the cosmic ray muon scattering imaging method includes the following steps 201 to 203.

[0032] Step 201: Obtain direction information of muons in the area to be imaged. The direction information includes incident track information and outgoing track information.

[0033] In scattering imaging, the density changes inside the material are inferred by measuring the distribution of scattering angles, and then the structural image is reconstructed. When muons pass through matter, they undergo multiple small-angle scattering with the Coulomb field of the atomic nucleus. Although a single scattering angle may obey the distribution described by the Rutherford scattering formula, under the superposition of a large number of independent random small-angle scatterings, according to the central limit theorem, the distribution of the total scattering angle will approach a Gaussian distribution. However, large-angle scattering events caused by hard scattering from atomic nuclei will exhibit a long-tailed distribution that deviates from the Gaussian assumption. If muon events scattered at large angles are used to reconstruct the scattering density, high-density artifacts may appear in the reconstructed image, and the overall image resolution will be reduced. Therefore, data screening is required.

[0034] To improve imaging accuracy, the present application further includes, between steps 201 and 202, determining, based on the direction information of muons within the region to be imaged, a scattering angle offset of each muon event after passing through the region to be imaged. Muon events are then screened based on the scattering angle offset of each muon event after passing through the region to be imaged.

[0035] In one specific application, the system calculates the scattering angle offset of each muon event after it passes through the imaging region based on the muon's incident and outgoing trajectory information. A statistical histogram of these scattering angle offsets is then plotted. This histogram is then used to filter out a certain percentage of large-angle scattered muon events, completing data filtering and improving subsequent imaging quality.

[0036] Step 202 : Calculate the three-dimensional most probable scattering path of the muon in the area to be imaged based on the direction information of the muon in the area to be imaged, based on Bayesian inference and the particle multi-Coulomb scattering distribution function.

[0037] Step 202 includes steps 21 and 22 .

[0038] Step 21 : Determine the track probability distribution function of the muon in the area to be imaged based on the direction information of the muon in the area to be imaged, Bayesian inference, and the particle multi-Coulomb scattering distribution function.

[0039] Specifically, if Figure 4 As shown, according to the direction information of the muon in the area to be imaged, a plane coordinate system is established with the muon incident direction as the positive direction of the Z axis and the incident point MI as the origin, and the coordinates of the incident point in the plane coordinate system are determined. (0,0,0) and the coordinates of the emission point ( ).in, is the total penetration thickness, is the total lateral displacement, Based on the incident point coordinates and the exit point coordinates, the track probability distribution function of the muon in the area to be imaged is determined using the Bayesian posterior probability and the particle multi-Coulomb scattering distribution function.

[0040] In the established plane coordinate system, the coordinates of the incident point and the exit point are known, so the coordinates of the middle point in the imaging area between the incident point and the exit point need to be solved. ( ), by substituting the incident point coordinates, exit point coordinates, incident direction, and exit direction into the track probability distribution function, we can solve the scattering state of the muon at different penetration depths (including lateral offset and angular offset), and then obtain the scattering track. is the penetration depth at the midpoint, is the lateral displacement of the midpoint, is the deflection angle of the midpoint.

[0041] The mathematical form of the Bayesian posterior probability is:

[0042] ;

[0043] in, is the posterior probability, is the likelihood probability, is the prior probability, is the probability of evidence, The probability distribution of represents the probability density of muon tracks in the imaged area, which is specifically manifested as the probability density of muons at different penetration depths. Down The posterior probability distribution of .

[0044] By substituting the particle multi-Coulomb scattering distribution function, the coordinates of the incident point and the exit point into the Bayesian posterior probability formula, the track probability distribution function can be derived.

[0045] In a specific application example, the particle multi-Coulomb scattering distribution function can take the following form:

[0046] ;

[0047] The above particle multi-Coulomb scattering distribution function is the distribution function ignoring the muon energy loss, where , , Muon velocity is the muon momentum, Z is the thickness of the object through which the track passes. When the material is uniform, the unit is the radiation length. When the material is non-uniform, the unit is the weighted average of the radiation length. is the lateral offset of the track, is the deflection angle of the track in the XOZ plane.

[0048] The probabilities in the Bayesian posterior probability are expressed using the distribution function:

[0049] ;

[0050] ;

[0051] .

[0052] Substituting the coordinates of the incident point and the exit point into the above formula, we can obtain the track probability distribution function:

[0053] ;

[0054] ;

[0055] ;

[0056] ;

[0057] ;

[0058] ;

[0059] ;

[0060] ;

[0061] ;

[0062] ;

[0063] in, 、 、 、 、 、 、 、 、 、 、 All are intermediate amounts.

[0064] Step 22: Solve the most likely value of the track probability distribution function and calculate the lateral offset of the muon at different penetration depths in the imaging area. and angular offset , in order to obtain the most probable three-dimensional scattering path of muons in the area to be imaged.

[0065] The penetration depth of the middle point The corresponding lateral offset By performing trajectory fitting, the most probable scattering trajectory of the muon in the XOZ plane can be reconstructed. Since the transport process of the muon in the YOZ plane is statistically independent of the XOZ plane, the lateral offset of the YOZ plane can be solved based on the same principle. , thereby obtaining the most probable three-dimensional scattering path of muons in the area to be imaged.

[0066] Step 203 : reconstructing a scattering density image in the area to be imaged based on the direction information of the muons in the area to be imaged and the three-dimensional scattering most probable track.

[0067] In a specific application example, step 203 includes the following steps 31 to 35.

[0068] Step 31: Divide the three-dimensional space of the area to be imaged into a plurality of voxels. The muon passage path within the voxel is the line integral of the three-dimensional scattering most probable path.

[0069] Step 32 : For any voxel, according to the direction information of the muons in the area to be imaged and the three-dimensional scattering most probable path, determine the scattering points in the voxel and the incident direction vector and the outgoing direction vector of each scattering point.

[0070] Step 33 : Obtain the scattering angle of each scattering point in the voxel according to the angle between the incident direction vector and the outgoing direction vector of each scattering point in the voxel.

[0071] Step 34 : determining the scattering density of the voxel according to the spatial distribution of the scattering points in the voxel, the scattering angle of each scattering point, and the three-dimensional scattering most probable path.

[0072] As an optional implementation, for each muon event, the point on the three-dimensional most probable scattering trajectory closest to the muon's incident and outgoing tracks is selected as the scattering point. The scattering angle is then calculated by calculating the angle between the incident and outgoing direction vectors at the scattering point. Finally, the scattering density within each voxel is calculated by combining the spatial distribution of the scattering points, the scattering angle, and the three-dimensional most probable scattering trajectory.

[0073] Step 35 : reconstructing a scattering density image in the area to be imaged according to the scattering density of each voxel.

[0074] The scatter density image within the area to be imaged allows for direct observation of the material structure within the area. Furthermore, the scatter density is directly related to the atomic number density of the material, so the material in the area to be imaged can be identified by the scatter density.

[0075] After reconstructing the scatter density image, clustering and smoothing processing are performed on the scatter density in the scatter density image to eliminate abnormal points in the scatter density image and reduce background noise.

[0076] The overall image reconstruction process of this application includes: data reading - data processing - scattering track estimation - scattering density reconstruction - image filtering and display, etc.

[0077] (1) Data reading: The muon impact position data collected by the track detection module is used to reconstruct the muon's incident and exit track information in three-dimensional space.

[0078] (2) Data processing: Based on the obtained muon incident and exit track information, the total scattering angle offset of each muon event after passing through the imaging area is calculated. The scattering angle offsets of all muon events are counted, and a scattering angle histogram is plotted. A certain proportion of large-angle scattered muon events is filtered out. The data processing steps can be selected according to actual needs.

[0079] (3) Scattering track estimation: Based on Bayesian inference and particle multi-Coulomb scattering distribution function, the track probability distribution function is derived. By solving the most likely value of the track probability distribution function, the lateral offset of the muon at different penetration depths in the imaging area is calculated. and angular offset , and then fit Curve reconstruction of the three-dimensional scattering most probable path.

[0080] (4) Scattering density reconstruction: Calculate the scattering points based on the known muon direction information and the solved three-dimensional scattering most probable track; calculate the scattering density using the scattering points and the three-dimensional scattering most probable track.

[0081] (5) Image filtering and display: After reconstructing the image, the scattering density is clustered and filtered and smoothed to reduce background noise, eliminate abnormal points, and complete image display.

[0082] Under the premise of obtaining prior knowledge such as muon incident track and exit track, this application uses the particle multi-Coulomb scattering distribution function as the probability density function to construct a track probability distribution function based on Bayesian inference. By solving the most likely value of the track probability distribution function, the three-dimensional scattering most probable track of the muon in the area to be imaged is obtained, and then the scattering density in the imaging area is reconstructed. Among them, according to the actual application situation, it is possible to further select the total scattering angle offset of each muon event and draw a statistical histogram to filter out a certain proportion of large-angle muon scattering events, screen the data, and improve the data quality and accuracy. Based on the Bayesian inference track estimation algorithm, the most probable track is calculated from the prior knowledge such as muon incident and exit track, which can effectively reconstruct the scattering point, improve the utilization efficiency of the muon event, avoid the problem of scattering points leaving the area to be imaged, and in the process of calculating the scattering density, the estimated three-dimensional scattering most probable track is used to calculate the scattering density, which can avoid track linearization processing, fit the real scattering track more closely, and improve the image quality.

[0083] Based on the same inventive concept, embodiments of the present application also provide a cosmic ray muon scattering imaging system for implementing the aforementioned cosmic ray muon scattering imaging method. The solution provided by this system is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more cosmic ray muon scattering imaging system embodiments provided below can be found in the above-described limitations of the cosmic ray muon scattering imaging method and will not be further elaborated here.

[0084] In an exemplary embodiment, Figure 5 As shown, a cosmic ray muon scattering imaging system is provided, which includes: a muon direction acquisition module 501, a scattering track estimation module 502 and a scattering density reconstruction module 503.

[0085] The muon direction acquisition module 501 is used to obtain the direction information of the muon in the area to be imaged. Specifically, the incident direction of the muon and the exit direction after passing through the area to be imaged are obtained by the track detection device, and this information is input into the scattering track estimation module as prior knowledge.

[0086] The scattering track estimation module 502 is used to calculate the three-dimensional most probable scattering track of muons in the area to be imaged based on the direction information of muons in the area to be imaged, Bayesian inference and particle multi-Coulomb scattering distribution function.

[0087] The scattering density reconstruction module 503 is used to reconstruct a scattering density image in the area to be imaged based on the direction information of muons in the area to be imaged and the three-dimensional scattering most probable path.

[0088] In a specific application example, the scattering density reconstruction module inputs the direction information obtained by the muon direction acquisition module and the three-dimensional scattering most probable track determined by the scattering track estimation module into the scattering density reconstruction algorithm to complete the muon scattering density reconstruction and graphical display.

[0089] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6As shown. The computer device includes a processor, memory, an input / output (I / O) interface, and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device provides computing and control capabilities. The processor internally includes a muon direction acquisition module, a scattering track estimation module, and a scattering density reconstruction module. These three modules execute the muon scattering imaging algorithm. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and computer program in the non-volatile storage medium. The database of the computer device stores muon direction information within the area to be imaged. The I / O interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via a network connection. When executed by the processor, the computer program implements a cosmic ray muon scattering imaging method.

[0090] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0091] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0092] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0093] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0094] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0095] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0096] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A cosmic ray muon scattering imaging method, characterized in that: The cosmic ray muon scattering imaging method comprises: Obtaining direction information of muons in the area to be imaged; the direction information includes incident track information and outgoing track information; According to the direction information of the muon in the area to be imaged, the scattering angle offset of each muon event after passing through the area to be imaged is determined; Muon events are screened based on the scattering angle offset of each muon event after it passes through the area to be imaged; According to the direction information of the muon in the imaging area, a plane coordinate system is established with the muon incident direction as the positive direction of the Z axis and the incident point MI as the origin, and the coordinates of the incident point in the plane coordinate system are determined. (0,0,0) and the coordinates of the emission point ( );in, is the total penetration thickness, is the total lateral displacement, is the cumulative deflection angle; the coordinates of the exit point include the total penetration thickness, the total lateral displacement and the cumulative deflection angle; Based on the coordinates of the incident point and the coordinates of the exit point, the track probability distribution function of the muon in the area to be imaged is determined using the Bayesian posterior probability and the particle multi-Coulomb scattering distribution function; Solving the most likely value of the track probability distribution function and calculating the lateral offset and angular offset of the muon at different penetration depths in the area to be imaged to obtain the most likely three-dimensional scattering track of the muon in the area to be imaged; Dividing the three-dimensional space of the area to be imaged into a plurality of voxels; For any voxel, the scattering points within the voxel and the incident and outgoing direction vectors of each scattering point are determined based on the muon direction information within the imaged area and the three-dimensional scattering most probable trajectory. For each muon event, the point on the three-dimensional scattering most probable trajectory closest to the muon incident and outgoing tracks is selected as the scattering point. The scattering angle is then calculated by calculating the angle between the incident and outgoing direction vectors of the scattering point. Finally, the scattering density within each voxel is calculated by combining the spatial distribution of the scattering points, the scattering angle, and the three-dimensional scattering most probable trajectory. A scatter density image of the area to be imaged is reconstructed based on the scatter density of each voxel.

2. A cosmic ray muon scattering imaging system, applied to the cosmic ray muon scattering imaging method according to claim 1, characterized in that: The cosmic ray muon scattering imaging system comprises: A muon direction acquisition module is used to obtain the direction information of muons in the area to be imaged; The scattering track estimation module is used to calculate the three-dimensional most probable scattering track of muons in the area to be imaged based on the direction information of muons in the area to be imaged, Bayesian inference and particle multi-Coulomb scattering distribution function; The scattering density reconstruction module is used to reconstruct the scattering density image in the area to be imaged based on the direction information of the muons in the area to be imaged and the three-dimensional scattering most probable track.

3. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the cosmic ray muon scattering imaging method described in claim 1.

Images