A method for converting CT images into cosRMC input files

Abstract

The present invention relates to the field of CT image modeling technology, and in particular to a method for converting CT images into a Monte Carlo simulation cosRMC input file. The technical solution comprises the following steps: dividing the entire HU value range into 35 segments, each corresponding to a human tissue with different densities, and setting the corresponding elemental composition mass percentages. The present invention converts CT images into a Monte Carlo simulation cosRMC input file. By dividing the HU value range, extracting and utilizing image data, accurately constructing a geometric model, utilizing a repeating structure function, flexibly setting source information and counters, and performing flux-dose conversion, the method achieves precise simulation of human tissue characteristics, efficient data processing, accurate model construction, and comprehensive statistical information. Ultimately, this method facilitates precise dose calculation and provides a strong guarantee for the formulation of BNCT treatment plans.

Description

Technical Field

[0001] The present invention relates to the technical field of CT image modeling, and in particular to a method for converting a CT image into a Monte Carlo procedure cosRMC input file. Background Art

[0002] Boron neutron capture therapy (BNCT) is a precision cancer treatment that uses neutrons to 10 The B reaction releases highly lethal alpha particles and 7 Li ions are used to destroy tumor cells. The key to this therapy lies in the ability of boron-containing drugs to selectively accumulate in tumor tissue, achieving targeted killing of tumor cells while minimizing damage to surrounding healthy tissue. To ensure the effectiveness and safety of BNCT, accurate dose calculation and treatment planning are crucial, and this is inseparable from the application of medical imaging technology and Monte Carlo methods.

[0003] The role of medical imaging in the BNCT treatment planning system (TPS):

[0004] Constructing a 3D human body model: Medical images of patients, such as CT or MRI, provide basic data for constructing an accurate 3D human body model. By segmenting and reconstructing the image data, a geometric model containing different tissues and organs can be obtained. For example, based on CT images in DICOM format, a corresponding voxel model can be established in a Monte Carlo program (such as cosRMC). Figure 3 shown.

[0005] Target and OAR delineation: On medical imaging, doctors can accurately delineate the tumor target volume and surrounding OARs, such as the eyes and brainstem. This information is used for subsequent dose calculations and treatment plan optimization, ensuring effective irradiation of the tumor while avoiding unacceptable damage to OARs.

[0006] Dose distribution assessment and visualization: Monte Carlo simulations are used to simulate the interaction between neutrons and human tissue, calculating dose deposition in different tissues and overlaying the resulting dose distribution with the original medical image. This allows physicians to intuitively assess the dose distribution of the treatment plan, determining whether the target area receives an adequate dose and whether organs at risk are over-irradiated.

[0007] The role of HU value in BNCT dose calculation:

[0008] CT images reveal the varying degrees of absorption of X-rays by different tissues after they pass through the human body. Due to differences in the composition and density of human tissues, X-ray absorption varies, resulting in varying intensities of X-rays after they penetrate the body. To quantify these variations, Hounsfield units (HU) or CT values ​​are used to represent the numerical values ​​in CT images, reflecting the degree of X-ray absorption by tissues.

[0009] Different tissues have different absorption levels of X-rays. This is due to the X-ray attenuation coefficient. X-rays decay exponentially after passing through an object, which can be expressed by the formula:

[0010] I=I0e -μx

[0011] It can be expressed as follows: μ represents the linear attenuation coefficient of X-rays in tissue, x is the distance the X-rays travel in the homogeneous medium, I0 represents the intensity of the X-rays at initial incidence, and I represents the intensity of the X-rays after attenuation by the material. This formula describes the attenuation law of X-rays of constant energy after passing through a homogeneous medium. The attenuation coefficient μ can be expressed as:

[0012]

[0013] Also known as the absorption coefficient, the unit is m -1 (SI), a characteristic parameter of a substance, is a constant determined by the substance's inherent properties, such as density, elemental composition, and energy. Therefore, substances composed of different materials typically have different attenuation coefficients.

[0014] In actual medical applications, the tissue attenuation coefficient is not used directly to reflect tissue density, but the CT value is used. The attenuation coefficient of each substance and the CT value have the following relationship:

[0015]

[0016] where μ w is the attenuation coefficient of water, CT x corresponds to an attenuation coefficient of μ xThe HU value of a substance. In this definition, the HU value of water is always defined as 0. This is because the density of human tissue is closest to that of water, so water's X-ray absorption capacity is used as an intermediate reference. Specifically, higher tissue density and a higher effective atomic number result in a higher attenuation coefficient, greater than that of water, and a positive HU value greater than 0. Lower tissue density and a lower effective atomic number also result in a lower attenuation coefficient, less than that of water, and a negative HU value less than 0. The upper limit of HU is +1000 for compact bone, the densest tissue in the human body, and the lower limit is -1000 for air, the lowest density. Therefore, the HU scale has a total of 2000 units. The CT values ​​of various human tissues are distributed between -1000 HU and +1000 HU. Different grayscale values ​​can be used to correspond to HU to distinguish tissues of varying densities. The CT values ​​of normal human tissues are listed in the table below.

[0017]

[0018] Advantages of the Monte Carlo method in BNCT dose calculation:

[0019] Accurate simulation of particle transport: The Monte Carlo method can accurately simulate the transport process of neutrons in human tissue, including scattering, absorption, and nuclear reactions, by simulating the random walk trajectories of a large number of particles.

[0020] Considering complex geometries: Monte Carlo methods can handle complex human body geometries, such as voxel models built from medical images, to more accurately calculate dose distributions.

[0021] Simulating multiple particle types: During BNCT treatment, a variety of particles are generated, such as neutrons, alpha particles, and 7Li ions. Monte Carlo methods can simulate the transport process of all these particles and calculate their dose contribution to tissues.

[0022] Application of DICOM format medical images in BNCT:

[0023] Storing complete patient information:

[0024] DICOM (Digital Imaging and Communications in Medicine) is a standard format for medical imaging. It not only contains image data, but also includes the patient's demographic information, scanning parameters, etc., providing more comprehensive information for dose calculation and treatment planning.

[0025] Convenient data exchange and processing: Medical images in DICOM format can be exchanged and processed between different medical imaging devices and software, such as importing CT images into Monte Carlo programs for dose calculation.

[0026] Support for automated modeling: Based on DICOM images, automated modeling tools can be developed to convert medical imaging data into the geometric models required by the Monte Carlo program, thereby improving modeling efficiency and accuracy.

[0027] Medical imaging and Monte Carlo methods play a crucial role in BNCT treatment. Medical imaging provides information for constructing human body models and delineating target volumes and organs at risk, while Monte Carlo methods accurately simulate particle transport, calculate dose distributions, and evaluate the effectiveness of treatment plans. DICOM-formatted medical images provide a standardized platform for data storage, exchange, and processing, facilitating the automation and precision of BNCT treatment planning. With the continuous advancement of medical imaging technology and Monte Carlo methods, BNCT treatment will become more precise, safe, and effective, bringing greater hope to cancer patients.

[0028] Therefore, the present application proposes a method for converting CT images into a Monte Carlo simulation cosRMC input file. Summary of the Invention

[0029] The purpose of the present invention is to address the problem in the background technology that there is a need for more accurate, safe and effective BNCT treatment methods, and to propose a method for converting CT images into Monte Carlo simulation cosRMC input files.

[0030] The technical solution of the present invention is a method for converting a CT image into a Monte Carlo simulation cosRMC input file, comprising the following steps:

[0031] Step 1: Divide the entire HU value range into 35 segments, corresponding to human tissues with different densities, and set the corresponding element composition mass percentages;

[0032] Step 2: Convert the material information defined in step 1 into text information that can be read by the cosRMC program;

[0033] Step 3: Use the VTK visualization toolkit to parse the DICOM format CT image and extract the HU value of each voxel in the CT image;

[0034] Step 4: Establish a correspondence between the extracted HU values ​​and the corresponding position information in the CT image to produce a three-dimensional matrix set;

[0035] Step 5: Use the surface function of the cosRMC program to create a geometric surface with the same size as the voxel size of the CT image;

[0036] Step 6, using the cell function of the cosRMC program, the geometric surface in step 5 is generated into a geometric cell;

[0037] Step 7, using the repeating structure function in the cosRMC program, fill the three-dimensional matrix set into the cosRMC input file;

[0038] Step 8: Write the fixed source module of cosRMC according to the source distribution required by the specific problem, and determine the source type and source strength information;

[0039] Step 9: Overlay a counting grid in cosRMC. The size and number of the grid are consistent with the voxels of the CT image. This grid is used for neutron transport calculation and neutron flux statistics.

[0040] Step 10, using the flux-dose conversion factor, calculate the dose distribution.

[0041] Optionally, the size of the CT image is 512 mm × 512 mm, the scanning interval is 1 mm, and each grid cell and UNIVERSE in cosRMC should be consistent with the size of a voxel in the CT image, which is 1 mm × 1 mm.

[0042] Optionally, the total size of the cosRMC repetitive structure should be consistent with the total size of the CT image, which is 512 mm × 512 mm, and filled with 512 UNIVERSEs of size 1 mm × 1 mm in the X and Y directions respectively.

[0043] Optionally, in step 2, the input card format of common materials is: Mat<mat_id> <density><zaid.xxx> <fraction>Mat is the keyword for the material input card, mat_id is the material number, and density is the total density of the material; density>0 indicates atomic density, with the unit being 1024 atoms / cm3; density<0 indicates mass density, with the unit being g / cm3; zaid.xxx specifies the ACE cross-section database corresponding to the nuclide, where zaid is the nuclide ID and the suffix .xxx specifies the type of cross-section database; fraction is the proportion of the nuclide in the material. If fraction>0 indicates the atomic density share, and if fraction<0 indicates the mass density share, the fractions in the same material must have the same sign.

[0044] Optionally, in step 5, the surface is the most basic unit of cosRMC geometric description, and the specific format is: Surf <id> <tpye> <params>Surf is the keyword for the surface input card, id is the surface number, which must be a positive integer and no duplication is allowed, type is the keyword corresponding to the surface type, and params is the surface equation parameters.

[0045] Optionally, in step 6, define a cell in cosRMC, the specific format is: Cell <id>{surf_bool_definition}{cell_info}, where Cell is the cell input card keyword, id is the cell number, which must be a positive integer and cannot be repeated, surf_bool_definition refers to the surface Boolean definition of the cell, which consists of a directional surface and a Boolean operator and is used to define the cell area, and cell_info defines the physical and geometric parameters of the cell, including material, volume, temperature, and layer filling information. The specific format is: [Mat= <id>][Vol= <vol>][Tmp= <tmp>][Void= <flag>][Fill= <fillparam>][Inner= <flag>]. Among them, the Mat tab defines the filling material of the cell, and the default value is Mat = 0 (vacuum). The Vol tab defines the volume of the cell, in cm3, and the default value is Vol = 1.0 cm3. The Tmp tab defines the temperature of the cell, in K, and the default value is Tmp = 293.6 (K). When the temperature of the material filled in the cell does not match the cell temperature, the nuclide cross section will be Doppler broadened. The Void tab specifies whether to stop tracking after the neutron enters the cell, which is mainly used to describe the area outside the vacuum boundary. Void = 0 (default value), the neutron continues to track after entering the area; Void = 1, the neutron stops tracking after entering the area; the Fill tab defines the space filled inside the cell. The Inner tab specifies whether the cell is an internal cell, that is, it is not divided by the outer boundary during the filling process. Inner = 0 (default value) indicates a non-interior cell, and Inner = 1 indicates an interior cell.

[0046] Optionally, in step 7: the space of the repeating structure is composed of regularly arranged grids, the quadrilateral repeating grid is established in the xyz coordinate system, the coordinate origin O is established at the lower left corner of the first grid, the first grid is numbered 1, and the input format of the quadrilateral repeating structure is: Universe...[Lat=1][Scope= <xnumynumznum>][Pitch= <xlenylenzlen>][Fill=<U1U2...Um> ], Lat=1 indicates that the repeating structure type is a quadrilateral, the Scope tab defines the number of repeated grids in the x, y, and z directions, the Pitch tab defines the width of the repeated grid in the x, y, and z directions, the parameters must be positive, Fill indicates the definition of the number of spaces filled in the grid in sequence, there are a total of m=xNum×yNum×zNum numbers, and the filling order is: fill in the x direction first, then fill in the y direction, and finally fill in the z direction.

[0047] Optionally, the step 8 specifically includes: setting fixed source information, the format of which is: FixedSourceparticlepopulation= <n>fission= <fnfp>FixedSource is the keyword for the fixed source module. Particle indicates that the content of this card is used to describe some parameters of the initial source particles. Population refers to the number of initial particles in the fixed source calculation. Fission specifies whether to turn off fission neutrons and photons. If fN is 0, it means turning off fission neutrons. If fN is 1, it means turning on fission neutrons. If fP is 0, it means turning off fission photons. If fP is 1, it means turning on fission photons.

[0048] Optionally, in step 9, the input card of the mesh counter is: MeshTally <id>[Particle= <par>][Normalize= <nor>][Type= <type>][Energy=<erg_bin>][Geometry= <geo>]IMeshNum= <nums>IMeshBound= <bounds>=MeshNum= <nums>JMeshBound= <bounds>=KMeshNum= <nums>KMeshBound= <bounds>.

[0049] Optionally, MeshTally is the keyword of the mesh counter input card, id is the number of the mesh counter, Particle card specifies the statistical particle type, 1 represents neutrons, 2 represents photons; Normalize determines whether to use mesh volume for normalization; Normalize = 1 is used, Normalize = 0 is not used; Type card specifies the counting type, 1 represents neutron or photon flux, 2 represents neutron fission power, 3 represents neutron fission reaction rate, 4 represents neutron absorption reaction rate, 5 represents fission neutron production rate, 6 represents neutron or photon heat release rate; Energy tab specifies the energy interval of cluster counting, the parameter is energy interval point (Mev).

[0050] Compared with the prior art, this application has at least one of the following beneficial technical effects:

[0051] By dividing the HU value into intervals and setting the corresponding material information, and then converting it into a format recognizable by the program, the characteristics of different human tissues can be accurately reflected, making the simulation more realistic and laying the foundation for accurate dose calculation.

[0052] With the help of the VTK visualization toolkit, HU values ​​are extracted and a three-dimensional matrix set containing position information is established, which is conducive to building an accurate human body model and ensuring that subsequent simulations and calculations are carried out around the accurate human tissue geometry.

[0053] Define surfaces and grid elements at appropriate scanning intervals to clearly and accurately describe tissue boundaries and regions, improve the accuracy of dose calculations for different tissue locations, and enhance the effectiveness of treatment plans.

[0054] By using the repetitive structure function to fill the space and the regularly arranged grid, not only can an accurate and complete human body model be quickly constructed, but it also facilitates statistical analysis of relevant information during calculations, promoting the efficiency of the entire process.

[0055] Fixed source information can be flexibly set as needed, and key information such as neutron flux can be counted from multiple aspects through grid counters, providing detailed and practical data support for dose calculation in all aspects.

[0056] Calculating dose distribution based on the flux-dose conversion factor helps accurately determine the effectiveness and safety of treatment plans, helping doctors develop more accurate and reasonable BNCT treatment plans, and promoting the development of treatment in a precise, safe, and effective direction.

[0057] The present invention converts CT images into Monte Carlo simulation program cosRMC input files. Through multiple measures such as HU value interval division, image data extraction and utilization, precise construction of geometric models, repeated structure function, flexible setting of source information and counters, and flux-dose conversion, it achieves precise simulation of human tissue characteristics, efficient data processing, accurate model construction, and comprehensive statistical information. Ultimately, it helps to accurately calculate the dose and provide strong support for the formulation of BNCT treatment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A flow chart of a method for converting CT images into input files for the Monte Carlo procedure cosRMC;

[0059] Figure 2 Schematic diagram of the repeated structure method in cosRMC;

[0060] Figure 3 Generates voxel model maps for the Monte Carlo program cosRMC. DETAILED DESCRIPTION

[0061] The technical solution of the present invention is further described below with reference to the accompanying drawings and specific embodiments.

[0062] Example

[0063] like Figure 1 As shown in FIG, the present invention proposes a method for converting a CT image into a Monte Carlo procedure cosRMC input file, and each step is described in detail below.

[0064] Step 1: Take the lower limit of HU value -1000 and the upper limit of HU value +1000 as the value range, divide it into 35 intervals, and set different material information for each HU interval, including material density and element composition percentage.

[0065] Step 2: Convert the material information into a text format that can be recognized by the cosRMC program. The input card format for common materials is: Mat<mat_id> <density><zaid.xxx> <fraction>Mat is the key word for the material input card, mat_id is the material ID, and density is the total material density. density > 0 indicates atomic density, in units of 1024 atoms / cm³; density < 0 indicates mass density, in units of g / cm³. zaid.xxx specifies the ACE cross-section database corresponding to the nuclide, where zaid is the nuclide ID and the suffix .xxx specifies the cross-section database type. fraction is the proportion of the nuclide in the material. If fraction > 0, it indicates the atomic density fraction (relative value); if fraction < 0, it indicates the mass density fraction (relative value). Fractions in the same material must have the same sign.

[0066] Step 3: Import the medical CT image in DICOM format into the VTK visualization toolkit, extract the HU value of each voxel in the CT image, and obtain an array containing HU value data.

[0067] Step 4: The extracted HU values ​​are mapped to the positional information of the CT images to generate a 3D matrix containing the positional information. Depending on the user's needs, a different number of CT images can be stacked. For example, a matrix generated from 20 CT images would be 512*512*20.

[0068] Step 5: Surface is the most basic unit of cosRMC geometry description. The specific format is: Surf <id> <tpye> <params>Surf is the keyword for the surface input card, id is the surface ID (a positive integer with no duplication), type is the keyword corresponding to the surface type, and params is the surface equation parameters. Since the CT image scan interval is 1 mm, a surface of the corresponding size must also be created in cosRMC.

[0069] Step 6, define the grid cell in cosRMC, the specific format is: Cell <id>{surf_bool_definition}{cell_info}. Cell is the keyword of the cell input card, id is the cell number, which must be a positive integer and cannot be repeated. surf_bool_definition refers to the surface Boolean definition of the cell, which consists of a directional surface and a Boolean operator and is used to define the cell area. cell_info defines other related information about the cell.

[0070] Step 7, use the repeat structure function of cosRMC, such as Figure 2 As shown, the three-dimensional matrix composed of CT values ​​is filled into the space. A repeating structure is a special type of space that consists of regularly arranged grids. The quadrilateral repeating grid is established in the xyz coordinate system, with the coordinate origin O established at the lower left corner of the first grid (numbered 1). The input format of the quadrilateral repeating structure is: Universe...[Lat=1][Scope= <xnumynumznum>][Pitch= <xlenylenzlen>][Fill=<U1U2...Um> ]. Lat = 1 indicates that the repeating structure type is a quadrilateral. The Scope tab defines the number of repeating grids in the x, y, and z directions. The Pitch tab defines the width of the repeating grid in the x, y, and z directions. The parameters must be positive. Fill represents the number of spaces (universes) to be filled in the grid in sequence. There are a total of m = xNum × yNum × zNum numbers. The filling order is: fill in the x direction first, then fill in the y direction, and finally fill in the z direction.

[0071] Step 8, set the fixed source information, the format is: FixedSourceparticlepopulation= <n>fission= <fnfp>FixedSource is the keyword for the fixed source module, Particle indicates that the contents of this card are used to describe some parameters of the initial source particles, population refers to the number of initial particles in the fixed source calculation, and fission specifies whether to turn off fission neutrons and photons. If fN is 0, it means turning off fission neutrons, and fN is 1, it means turning on fission neutrons. If fP is 0, it means turning off fission photons, and if fP is 1, it means turning on fission photons.

[0072] Step 9: Set up a grid counter to count the neutron flux information in the grid. The input card of the grid counter is:

[0073] MeshTally <id>[Particle= <par>][Normalize= <nor>][Type= <type>][Energy=<erg_bin>][Geometry= <geo>]IMeshNum= <nums>IMeshBound= <bounds>=MeshNum= <nums>JMeshBound= <bounds>=KMeshNum= <nums>KMeshBound= <bounds>. Among them, MeshTally is the keyword of the mesh counter input card, id is the number of the mesh counter, and the Particle card specifies the statistical particle type, 1 represents neutrons, and 2 represents photons. Normalize determines whether to use the mesh volume for normalization. Normalize = 1 is used, Normalize = 0 is not used. The Type card specifies the counting type, 1 represents neutron or photon flux, 2 represents neutron fission power, 3 represents neutron fission reaction rate, 4 represents neutron absorption reaction rate, 5 represents fission neutron production rate, and 6 represents neutron or photon heat release rate. The Energy tab specifies the energy interval for cluster counting, and the parameter is the energy interval point (Mev). For example, "Energy = 6.25E-720" means that the counting interval is 0 to 0.625ev, 0.625ev to 20Mev, and 20Mev to positive infinity, a total of 3 intervals. IMeshNum and IMeshBound specify the sequence of mesh numbers and mesh boundaries in the I direction, respectively. The number of mesh boundaries must be one more than the number of meshes, and the mesh boundary values ​​must be monotonically increasing. For example, IMeshNum = 23 IMeshBound = 158 means that coordinates 1 to 5 in the I direction are evenly divided into two meshes, and coordinates 5 to 8 are evenly divided into three meshes. The same applies to JMeshNum, JMeshBound, KMeshNum, and KMeshBound.

[0074] Step 10: Calculate the dose distribution using the flux-dose conversion factor.

[0075] In this implementation, this multi-interval division is used: the HU value, with a lower limit of -1000 and an upper limit of +1000, is divided into 35 intervals. Different material information, including material density and elemental composition percentages, is assigned to each interval. This allows for a more detailed reflection of the characteristics of different human tissues, making subsequent dose calculations more accurate. Converting this material information into a text format recognizable by the cosRMC program facilitates the program's identification and processing of different materials, improving calculation efficiency and accuracy.

[0076] In this example, the VTK visualization toolkit is used to extract the HU value of each voxel in the CT image, enabling rapid and accurate acquisition of key image data, providing a reliable data foundation for subsequent processing. The extracted HU values ​​are then mapped to the positional information in the CT image, generating a three-dimensional matrix containing this positional information. This facilitates the accurate construction of a human body model in cosRMC, better simulating the geometric structure of human tissue.

[0077] Furthermore, in cosRMC, surfaces of corresponding sizes are established at 1mm scanning intervals. These surfaces, as the most basic unit of geometric description, more precisely describe the boundaries and shapes of human tissue, providing a more accurate geometric model for subsequent dose calculations. By defining grid elements, the division of each region and related information are clarified, enabling more precise analysis of different regions in dose calculations, improving the relevance and effectiveness of treatment plans.

[0078] It is worth noting that by utilizing the repetitive structure feature of cosRMC, a three-dimensional matrix composed of CT values ​​can be filled into space to rapidly construct a three-dimensional model of human tissue, improving modeling efficiency while ensuring model accuracy and completeness. The repetitive structure, composed of a regularly arranged grid, facilitates the statistics and analysis of information such as neutron flux during the calculation process, facilitating dose calculations.

[0079] In this embodiment, fixed source information is set to flexibly adjust the initial particle count and whether to disable fission neutrons and photons based on actual needs, making the simulation more realistic and improving the accuracy and reliability of dose calculations. Setting up a grid counter allows for multi-dimensional neutron flux statistics within the grid, including specifying the particle type, count type, energy range, and the number and boundary sequence of grids in each direction. This provides rich data support for comprehensive dose distribution assessment.

[0080] By calculating the dose distribution through the flux-dose conversion factor, neutron flux information can be accurately converted into dose information, providing a key basis for evaluating the effectiveness and safety of treatment plans and helping doctors develop more accurate treatment plans.

[0081] The above specific embodiments are merely several optional embodiments of the present invention. Based on the technical solutions of the present invention and the relevant inspirations of the above embodiments, those skilled in the art may make various alternative improvements and combinations to the above specific embodiments.< / bounds> < / nums> < / bounds> < / nums> < / bounds> < / nums> < / geo> < / type> < / nor> < / par> < / id> < / fnfp> < / n> < / xlenylenzlen> < / xnumynumznum> < / id> < / params> < / tpye> < / id> < / fraction> < / density> < / bounds> < / nums> < / bounds> < / nums> < / bounds> < / nums> < / geo> < / type> < / nor> < / par> < / id> < / fnfp> < / n> < / xlenylenzlen> < / xnumynumznum> < / flag> < / fillparam> < / flag> < / tmp> < / vol> < / id> < / id> < / params> < / tpye> < / id> < / fraction> < / density>

Claims

1. A method for converting a CT image into a Monte Carlo procedure cosRMC input file, characterized in that: The following steps are involved: Step 1: Divide the entire HU value range into 35 segments, corresponding to human tissues with different densities, and set the corresponding element composition mass percentages; Step 2: Convert the material information defined in step 1 into text information that can be read by the cosRMC program; Step 3: Use the VTK visualization toolkit to parse the DICOM format CT image and extract the HU value of each voxel in the CT image; Step 4: Establish a correspondence between the extracted HU values ​​and the corresponding position information in the CT image to produce a three-dimensional matrix set; Step 5: Use the surface function of the cosRMC program to create a geometric surface with the same size as the voxel size of the CT image; Step 6, using the cell function of the cosRMC program, the geometric surface in step 5 is generated into a geometric cell; Step 7, use the repeating structure function in the cosRMC program to fill the three-dimensional matrix set into the cosRMC input file. The space of the repeating structure is composed of regularly arranged grids. The quadrilateral repeating grid is established in the xyz coordinate system. The coordinate origin O is established at the lower left corner of the first grid. The first grid is numbered 1. The input format of the quadrilateral repeating structure is: Universe...[Lat=1][Scope= <xnumynumznum> ][Pitch=<xLenyLenzLe n> ][Fill=<U1 U2...Um> ], Lat=1 indicates that the repeating structure type is quadrilateral, the Scope tab defines the number of repeating grids in the x, y, and z directions, the Pitch tab defines the width of the repeating grid in the x, y, and z directions, and the parameter must be positive. Fill indicates the number of spaces filled in the grid in sequence, with a total of m=xNum×yNum×zNum numbers. The filling order is: fill in the x direction first, then fill in the y direction, and finally fill in the z direction;< / xnumynumznum> Step 8: Write the fixed source module of cosRMC according to the source distribution required by the specific problem, and determine the source type and source strength information; Step 9: Overlay a counting grid in cosRMC. The size and number of the grid are consistent with the voxels of the CT image. This grid is used for neutron transport calculation and neutron flux statistics. Step 10, using the flux-dose conversion factor, calculate the dose distribution.

2. The method for converting a CT image into a Monte Carlo procedure (cosRMC) input file according to claim 1, characterized in that: The size of the CT image is 512 mm × 512 mm, and the scanning interval is 1 mm. In cosRMC, each grid element and UNIVERSE should be consistent with the size of a voxel in the CT image, which is 1 mm × 1 mm.

3. The method for converting a CT image into a Monte Carlo procedure (cosRMC) input file according to claim 1, characterized in that: The total size of the cosRMC repetitive structure should be consistent with the total size of the CT image, which is 512 mm × 512 mm, and is filled with 512 UNIVERSEs of size 1 mm × 1 mm in the X and Y directions respectively.

4. A method for converting a CT image into a Monte Carlo procedure cosRMC input file according to claim 1, wherein in step 2, the input card format of common materials is: Mat<mat_id> <density><zaid.xxx> <fraction> Mat is the keyword for the material input card, mat_id is the material number, and density is the total density of the material; density>0 indicates atomic density, with the unit being 1024 atoms / cm3; density<0 indicates mass density, with the unit being g / cm3; zaid.xxx specifies the ACE cross-section database corresponding to the nuclide, where zaid is the nuclide ID and the suffix .xxx specifies the type of cross-section database; fraction is the proportion of the nuclide in the material. If fraction>0 indicates the atomic density share, and if fraction<0 indicates the mass density share, the fractions in the same material must have the same sign.< / fraction> < / density> 5. The method of converting a CT image into a Monte Carlo simulation cosRMC input file according to claim 1, characterized in that: In step 5, the surface is the most basic unit of cosRMC geometric description, and the specific format is: Surf <id> <tpye> <params>Surf is the keyword for the surface input card, id is the surface number, which must be a positive integer and no duplication is allowed, type is the keyword corresponding to the surface type, and params is the surface equation parameters.< / params> < / tpye> < / id> 6. The method of converting a CT image into a Monte Carlo procedure (cosRMC) input file according to claim 1, characterized in that: In step 6, define the cell in cosRMC. The specific format is: Cell <id> {surf_bool_definition}{cell_info}, where Cell is the keyword of the cell input card, id is the cell number, which must be a positive integer and no duplication is allowed, surf_bool_definition refers to the surface Boolean definition of the cell, which consists of a directional surface and a Boolean operator and is used to define the cell area, and cell_info defines the physical and geometric parameters of the cell.< / id> 7. The method of converting a CT image into a Monte Carlo procedure (cosRMC) input file according to claim 1, characterized in that: The step 8 specifically includes: setting fixed source information, the format is: FixedSourceparticlepopulation= <n>fission= <fnfp> FixedSource is the keyword for the fixed source module. Particle indicates that the content of this card is used to describe some parameters of the initial source particles. Population refers to the number of initial particles in the fixed source calculation. Fission specifies whether to turn off fission neutrons and photons. If fN is 0, it means turning off fission neutrons. If fN is 1, it means turning on fission neutrons. If fP is 0, it means turning off fission photons. If fP is 1, it means turning on fission photons.< / fnfp> < / n> 8. The method of converting a CT image into a Monte Carlo simulation cosRMC input file according to claim 1, characterized in that: In step 9, the input card of the mesh counter is: MeshTally <id>[Particle= <par>][Normalize= <nor>][Type= <type>][Energy=<erg_bin>][Geometry= <geo>]IMeshNum= <nums>IMeshBound= <bounds>=MeshNum= <nums>JMeshBound= <bounds>=KMeshNum= <nums>KMeshBound= <bounds> 。< / bounds> < / nums> < / bounds> < / nums> < / bounds> < / nums> < / geo> < / type> < / nor> < / par> < / id> 9. The method of converting a CT image into a Monte Carlo simulation cosRMC input file according to claim 8, characterized in that: MeshTal ly is the keyword of the mesh counter input card, id is the number of the mesh counter, Particle card specifies the statistical particle type, 1 represents neutrons, 2 represents photons; Normalize determines whether to use mesh volume for normalization; Normalize = 1 is used, Normalize = 0 is not used; Type card specifies the counting type, 1 represents neutron or photon flux, 2 represents neutron fission power, 3 represents neutron fission reaction rate, 4 represents neutron absorption reaction rate, 5 represents fission neutron production rate, and 6 represents neutron or photon heat release rate; Energy tab specifies the energy interval of cluster counting, and the parameter is the energy interval point.

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