Method for converting CT image into Monte Carlo program cosRMC input file

Abstract

The invention relates to the technical field of CT image modeling, in particular to a method for converting a CT image into a Monte Carlo program cosRMC input file. According to the technical scheme, the method comprises the following steps: dividing the whole HU value domain into 35 sections, and setting corresponding element composition mass percentages corresponding to human tissues with different densities respectively; according to the scheme of converting the CT image into a Monte Carlo program cosRMC input file, through measures including HU value interval division, image data extraction and utilization, accurate geometric model construction, repeated structure functions, flexible source information and counter setting, flux-dose conversion and the like, the method can be used for solving the problems in the prior art. Accurate simulation of human tissue characteristics, efficient data processing, accurate model construction and comprehensive statistical information are realized, accurate dose calculation is finally assisted, and powerful guarantee is provided for formulation of a BNCT treatment plan.

Description

Technical Field

[0001] The 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 is that boron-containing drugs can be selectively enriched in tumor tissue, thereby achieving targeted killing of tumor cells while minimizing damage to surrounding healthy tissues. In order to ensure the effectiveness and safety of BNCT treatment, accurate dose calculation and treatment planning are crucial, which cannot be achieved without the application of medical imaging technology and Monte Carlo method.

[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 area and organs at risk delineation: In medical imaging, doctors can accurately delineate the tumor target area and surrounding organs at risk, such as eyes, brain stem, etc. This information will be used for subsequent dose calculation and treatment plan optimization to ensure effective irradiation of the tumor while avoiding unacceptable damage to organs at risk.

[0006] Dose distribution evaluation and visualization: The Monte Carlo method simulates the interaction between neutrons and human tissues, calculates the dose deposition in different tissues, and overlays the dose distribution results with the original medical images. Doctors can intuitively evaluate the dose distribution of the treatment plan, determine whether the target area receives enough dose, and whether the organs at risk are over-irradiated.

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

[0008] CT images reflect the differences in the degree to which X-rays are absorbed by different tissues after passing through the human body. Due to the different composition and density of human tissues, the absorption capacity of X-rays is also different, resulting in differences in the intensity of X-rays after penetrating the human body. In order to quantify this difference, Hounsfield units (HU) or CT values ​​are used to represent the numerical values ​​of CT images, reflecting the degree of tissue absorption of X-rays.

[0009] The degree of absorption of X-rays in different tissues varies, which comes from the attenuation coefficient of X-rays. After passing through an object, the X-rays decay exponentially, which can be expressed by the formula:

[0010] I=I0e -μx

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

[0012]

[0013] It can also be called the absorption coefficient, with the unit being m -1 (International System of Units), as a characteristic parameter of a substance, is a constant, which is determined by the material properties of the substance itself, such as tissue density, composition of constituent elements and energy. Therefore, substances composed of different materials usually have different attenuation coefficient values.

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

[0015]

[0016] where μ w is the attenuation coefficient of water, CT x corresponds to an attenuation coefficient of μ xHU value of a substance. In the definition of this relationship, 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 the absorption capacity of water for X-rays is used as an intermediate reference, that is, if the tissue density is higher and the effective atomic number is higher, the attenuation coefficient is higher and greater than that of water, then the HU value will also be greater than 0, which is a positive value; if the tissue density is lower and the effective atomic number is lower, the attenuation coefficient is also lower and less than that of water, then the HU value will also be less than 0, which is a negative value. Take the tissue with the highest material density in the human body, compact bone, as the upper limit of HU, with a value of +1000; take the air with the lowest density as the lower limit of HU, with a value of -1000. Therefore, there are 2000 units in total in HU, and the CT values ​​of various tissues in the human body are distributed between -1000HU and +1000HU. Different grayscales can be used to correspond to HU to distinguish and identify human tissues of different densities. The CT values ​​of normal human tissues are listed in the following table.

[0017]

[0018] Advantages of 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 tissues, 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, multiple particles are generated, such as neutrons, alpha particles, 7Li ions, etc. The Monte Carlo method can simulate the transport process of all these particles and calculate their dose contribution to the tissue.

[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 demographics 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 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 vital role in BNCT treatment. Medical imaging provides information for building human models and outlining target areas / organs at risk, while Monte Carlo methods can accurately simulate the particle transport process, calculate dose distribution, and evaluate the effectiveness of treatment plans. Medical images in DICOM format provide a standardized platform for data storage, exchange, and processing, promoting the automation and precision of BNCT treatment plans. With the continuous development of medical imaging technology and Monte Carlo methods, BNCT treatment will be more accurate, 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 procedure cosRMC input file. Summary of the invention

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

[0030] The technical solution of the present invention is a method for converting a CT image into a Monte Carlo procedure 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 percentage;

[0032] Step 2, converting 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, establishing a correspondence between the extracted HU value and the corresponding position information in the CT image to produce a three-dimensional matrix set;

[0035] Step 5, using the surface function of the cosRMC program, to create a geometric surface that is consistent with the voxel size of the CT image;

[0036] Step 6, using the cell function of the cosRMC program, generating geometric cells from the geometric surface in step 5;

[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, superimpose counting grids in cosRMC, the size and number of which are consistent with the voxels of the CT image, 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 repetitive structure of cosRMC should be consistent with the total size of the CT image, which is 512 mm × 512 mm, 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 of the material input card, mat_id is the material number, density is the total density of the material; density>0 indicates atomic density, the unit is 1024 atoms / cm3; density<0 indicates mass density, the unit is 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, 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 geometry description, and the specific format is: Surf <id> <tpye> <params>Surf is the keyword of 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, a cell is defined in cosRMC, and 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 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 repeated structure is composed of regularly arranged grids, the quadrilateral repeated 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 repeated structure is: Universe...[Lat=1][Scope= <xnumynumznum>][Pitch= <xlenylenzlen>][Fill=<U1U2...Um> ], Lat=1 means 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 grids in the x, y, and z directions, the parameters must be positive, Fill means defining the number of spaces 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.

[0047] Optionally, the step 8 specifically includes: setting fixed source information, the format is: FixedSourceparticlepopulation= <n>fission= <fnfp>FixedSource is the keyword of 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>JMeshNum= <nums>JMeshBound= <bounds>=MeshNum= <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, and 6 represents neutron or photon heat release rate; Energy tab specifies the energy interval of cluster counting, and the parameter is energy interval point (Mev).

[0050] Compared with the prior art, the present invention 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 that the program can recognize, 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 geometric structure of human tissue.

[0053] Define surfaces and grid elements with appropriate scanning intervals to clearly and accurately describe the boundaries and regions of human tissues, improve the accuracy of dose calculation for different tissue parts, and enhance the effectiveness of treatment plans.

[0054] By using the function of repeated structures to fill the space and using regularly arranged grids, not only can an accurate and complete human body model be quickly constructed, but it also facilitates statistical analysis of relevant information during calculations, making the entire process more efficient.

[0055] It can flexibly set fixed source information as needed, and use grid counters to count key information such as neutron flux from multiple aspects, providing detailed and practical data support for dose calculation in all aspects.

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

[0057] The solution of converting CT images into Monte Carlo program cosRMC input files, through HU value interval division, image data extraction and utilization, accurate construction of geometric models, repeated structure functions, flexible setting of source information and counters, and flux-dose conversion, etc., can achieve accurate simulation of human tissue characteristics, efficient data processing, accurate model construction, and comprehensive statistical information, ultimately facilitating accurate calculation of doses and providing 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 a CT image into a Monte Carlo program cosRMC input file;

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

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

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

[0062] Example

[0063] like Figure 1 As shown, the present invention proposes a method for converting a CT image into a Monte Carlo program 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 +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 keyword of 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 of 1024 atoms / cm3; density<0 indicates mass density, with the unit 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 type of cross-section database. fraction is the proportion of the nuclide in the material. If fraction>0 indicates the atomic density share (relative value), if fraction<0 indicates the mass density share (relative value), the 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: Establish a corresponding relationship between the extracted HU values ​​and the position information of the CT images to generate a three-dimensional matrix set containing the position information. According to user needs, different numbers of CT images can be selected for superposition, such as a matrix generated by 20 CT images with a number of 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 of the surface input card, id is the surface number, which must be a positive integer and cannot be repeated, type is the keyword corresponding to the surface type, and params is the surface equation parameters. Since the scanning interval of CT images is 1mm, a surface of the corresponding size must also be created in cosRMC.

[0069] Step 6: Define the 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 to define the cell area, and cell_info defines other relevant information of 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. The repeating structure is a special type of space, which is composed of regularly arranged grids. The quadrilateral repeating grid is established in the xyz coordinate system, and the coordinate origin O is 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> ]. Where 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, and the parameter must be positive. Fill indicates the number of spaces (universes) filled in the grid, and 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>Among them, FixedSource is the keyword of 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, and fN is 1, which 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>JMeshNum= <nums>JMeshBound= <bounds>=MeshNum= <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 of the cluster count, 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 1 more than the number of meshes, and the mesh boundary values ​​must increase monotonically. For example, IMeshNum = 23IMeshBound = 158, which means that the coordinates 1 to 5 in the I direction are evenly divided into 2 meshes, and the coordinates 5 to 8 are evenly divided into 3 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, through this multi-interval division: the lower limit of HU value -1000 and the upper limit +1000 are divided into 35 intervals, and different material information is set for each interval, including material density and element composition percentage, which can more finely reflect the characteristics of different tissues of the human body and make subsequent dose calculation more accurate. The material information is converted into a text format recognizable by the cosRMC program, which facilitates the program to identify and process different materials, and improves the calculation efficiency and accuracy.

[0076] In this embodiment, the VTK visualization toolkit is used to extract the HU value of each voxel in the CT image, which can quickly and accurately obtain the key data in the image, providing a reliable data basis for subsequent processing. The extracted HU value is established according to the position information of the CT image, and a three-dimensional matrix set containing the position information is generated, which is convenient for accurately building a human body model in cosRMC and better simulating the geometric structure of human tissue.

[0077] Furthermore, in cosRMC, a surface of corresponding size is established at a scanning interval of 1mm. As the most basic unit of geometric description, it can more accurately describe the boundaries and shapes of human tissues and provide a more accurate geometric model for subsequent dose calculation. By defining the grid element, the division of each area and related information are clarified, so that the dose calculation can be more accurately analyzed for different areas, improving the pertinence and effectiveness of the treatment plan.

[0078] It is worth noting that by using the repetitive structure function of cosRMC to fill the three-dimensional matrix composed of CT values ​​into the space, a three-dimensional model of human tissue can be quickly constructed, which improves the modeling efficiency and ensures the accuracy and integrity of the model. The repetitive structure is composed of regularly arranged grids, which facilitates the statistics and analysis of information such as neutron flux during the calculation process, providing convenience for dose calculation.

[0079] In this embodiment, by setting fixed source information, the initial particle number and whether to turn off fission neutrons and photons can be flexibly adjusted according to actual needs, making the simulation closer to the actual treatment situation and improving the accuracy and reliability of dose calculation. By setting a grid counter, the neutron flux information in the grid can be counted from multiple dimensions, including specifying the statistical particle type, counting type, energy interval, and the number of grids and boundary sequences divided in each direction, etc., providing rich data support for comprehensive evaluation of dose distribution.

[0080] By calculating the dose distribution through the flux-dose conversion factor, the 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 to develop more accurate treatment plans.

[0081] The above specific embodiments are only 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 can 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 percentage; Step 2, converting 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, establishing a correspondence between the extracted HU value and the corresponding position information in the CT image to produce a three-dimensional matrix set; Step 5, using the surface function of the cosRMC program, to create a geometric surface that is consistent with the voxel size of the CT image; Step 6, using the cell function of the cosRMC program, generating geometric cells from the geometric surface in step 5; Step 7, using the repeating structure function in the cosRMC program, fill the three-dimensional matrix set into the cosRMC input file; 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, superimpose counting grids in cosRMC, the size and number of which are consistent with the voxels of the CT image, for neutron transport calculation and neutron flux statistics; Step 10, using the flux-dose conversion factor, calculate the dose distribution.

2. A 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 cell 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 repetitive structure of cosRMC should be consistent with the total size of the CT image, which is 512 mm × 512 mm, 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 program 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 of the material input card, mat_id is the material number, density is the total density of the material; density>0 indicates atomic density, the unit is 1024 atoms / cm3; density<0 indicates mass density, the unit is 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, if fraction<0 indicates the mass density share, the fractions in the same material must have the same sign.< / fraction> < / density> 5. The method for converting a CT image into a Monte Carlo procedure cosRMC input file according to claim 1, characterized in that: In step 5, the surface is the most basic unit of cosRMC geometry description, and the specific format is: Surf <id> <tpye> <params> Surf is the keyword of 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 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: In step 7: the space of the repeated structure is composed of regularly arranged grids, the quadrilateral repeated 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 repeated structure is: Universe...[Lat=1][Scope= <xnumynumznum>][Pitch= <xlenylenzlen> ][Fill=<U1 U2...Um> ], Lat=1 means 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 grids in the x, y, and z directions, the parameters must be positive, Fill means defining the number of spaces 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.< / xlenylenzlen> < / xnumynumznum> 8. 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 of 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> 9. The method of converting a CT image into a Monte Carlo procedure 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>JMesh Num= <nums>JMeshBound= <bounds>=MeshNum= <nums>KMeshBound= <bounds> 。< / bounds> < / nums> < / bounds> < / nums> < / bounds> < / nums> < / geo> < / type> < / nor> < / par> < / id> 10. The method for converting a CT image into a Monte Carlo procedure cosRMC input file according to claim 9, characterized in that: 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, 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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