CN112001108B - Cone beam CT Monte Carlo simulation cluster parallel acceleration method and system - Google Patents
Cone beam CT Monte Carlo simulation cluster parallel acceleration method and system Download PDFInfo
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- 238000000342 Monte Carlo simulation Methods 0.000 title claims abstract description 32
- 238000000034 method Methods 0.000 title claims abstract description 27
- 230000001133 acceleration Effects 0.000 title claims abstract description 21
- 239000002245 particle Substances 0.000 claims abstract description 80
- 239000011159 matrix material Substances 0.000 claims abstract description 28
- 238000005070 sampling Methods 0.000 claims abstract description 17
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- 238000013507 mapping Methods 0.000 claims abstract description 9
- 238000004088 simulation Methods 0.000 claims description 25
- 238000009825 accumulation Methods 0.000 claims description 11
- 238000013170 computed tomography imaging Methods 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 3
- 238000007408 cone-beam computed tomography Methods 0.000 abstract description 15
- 238000003384 imaging method Methods 0.000 abstract description 11
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- 230000005540 biological transmission Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
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- G06T11/005—Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
Abstract
The invention belongs to the technical field of Monte Carlo simulation, and particularly relates to a cone beam CT Monte Carlo simulation cluster parallel acceleration method and system, wherein a host and each node in the cluster are configured with an information transfer interface MPI, the states of the host and the nodes connected into the cluster are determined, and each node is numbered; the host generates a plurality of parts of Monte Carlo program data which are consistent with the number of the nodes, and the initial number of sampling particles in each part of program data is set according to the number of the nodes; in the MIP command mode, the host controls each node to synchronously run Monte Carlo program data according to the initial number of the respective sampling particles, each node uses a memory mapping mechanism to read all particle track data into a memory, and the particle tracks are respectively put into grids according to the size of a set detector grid, so that the number of the particles in each grid is counted and a storage matrix is established; the host collects and synthesizes the node storage matrixes to draw images, so that the problems of low imaging speed and the like of the traditional Monte Carlo simulation CBCT are solved, and the reconstruction efficiency and performance of cone beam CT are improved.
Description
Technical Field
The invention belongs to the technical field of Monte Carlo simulation, and particularly relates to a cone beam CT Monte Carlo simulation cluster parallel acceleration method and system.
Background
Monte Carlo is the mainstream method for simulating radiation transport at present, and utilizes the principle of mathematical statistics to simulate the transport process of electrons and photons by adopting a random sampling method. In order to accurately simulate, hundreds of millions of particles need to be sampled, huge calculation resources and time are consumed by huge particle quantity, and the simulation time is a main defect of Meng Ka simulation and is also a limitation to large-scale application of the method in CBCT imaging simulation. MCNP is a large, comprehensive, general-purpose monte carlo procedure that is widely used for the problem of joint transport of neutrons, photons and electrons, as well as critical issues. MPI is a parallel development tool based on a message transfer mechanism, and can coordinate the parallel running achievements of single machine, multiple cores, clusters and the like.
The biggest problem faced by adopting single-machine simulation CBCT imaging is that the simulation speed is low, the FIR card with the MCNP can be used for simulating cone beam X-ray imaging, the simulation time of a single image of the FIR card is too long, the application value of the single image is greatly reduced, and more images are needed for CBCT to finish image reconstruction.
Disclosure of Invention
Therefore, the invention provides a cone beam CT Monte Carlo simulation cluster parallel acceleration method and system, which are used for solving the problems of low imaging speed and the like of the existing Monte Carlo simulation CBCT and improving the performance of a cone beam CT reconstruction system.
According to the design scheme provided by the invention, the cone beam CT Monte Carlo simulation cluster parallel acceleration method comprises the following steps:
in the cluster, a host and each node are configured with an information transfer interface MPI for coordinating parallel running states, the states of the host and the nodes connected into the cluster are determined according to an IP list of the information transfer interface MIP, and each node is numbered;
the host generates a plurality of Monte Carlo program data for cone beam CT simulation, the number of which is consistent with the number of the nodes, and the initial number of sampling particles in each program data is set according to the number of the nodes so as to run the simulation of the corresponding number of particles in the nodes;
in the MIP command mode, the host controls each node to synchronously run Monte Carlo program data according to the respective sampling particle initial numbers, and particle tracks for recording the distance coordinate characteristics of the detector and the ray source are generated; each node uses a memory mapping mechanism to read all particle track data into a memory, respectively puts the particle tracks into grids according to the size of a set detector grid, counts the number of particles in each grid, and establishes a storage matrix for storing the statistical data of the number of particles;
the host collects the storage matrix of each node and performs comprehensive accumulation on the storage matrix to obtain a graph data matrix for drawing the image.
As the cone beam CT Monte Carlo simulation cluster parallel acceleration method of the invention, further, each computer in the cluster is configured in the same network group, one computer is selected as a host, and the rest computers are used for each node.
As the cone beam CT Monte Carlo simulation cluster parallel acceleration method, the invention further traverses particle tracks, and projects each particle into a two-dimensional grid to count the number of grid particles.
As the cone beam CT Monte Carlo simulation cluster parallel acceleration method, the invention further adds the data of the same shape and position in the comprehensive accumulation of the storage matrix, and sets the gray scale range for displaying the picture so as to perform CT imaging.
Furthermore, the invention also provides a cone beam CT Monte Carlo simulation cluster parallel acceleration system, which comprises: a host and a plurality of nodes disposed within the cluster, wherein,
the host and each node are configured with an information transfer interface MPI for coordinating parallel running states, the states of the host and the nodes connected into the cluster are determined according to an IP list of the information transfer interface MIP, and each node is numbered;
the host generates a plurality of Monte Carlo program data for cone beam CT simulation, the number of which is consistent with the number of the nodes, and the initial number of sampling particles in each program data is set according to the number of the nodes so as to run the simulation of the corresponding number of particles in the nodes;
in the MIP command mode, the host controls each node to synchronously run Monte Carlo program data according to the respective sampling particle initial numbers, and particle tracks for recording the distance coordinate characteristics of the detector and the ray source are generated; each node uses a memory mapping mechanism to read all particle track data into a memory, respectively puts the particle tracks into grids according to the size of a set detector grid, counts the number of particles in each grid, and establishes a storage matrix for storing the statistical data of the number of particles;
the host collects the storage matrix of each node and performs comprehensive accumulation on the storage matrix to obtain a graph data matrix for drawing the image.
The invention has the beneficial effects that:
in the invention, under the support of MPI, a plurality of computers are utilized to realize parallel acceleration of the clusters, the speed of processing the Monte Carlo simulation CBCT particle track file is improved, and compared with a main-stream single-machine Monte Carlo simulation mode, the cluster mode can fully utilize the advantages of the computer clusters, and improve the efficiency and simulation performance of the Monte Carlo simulation CBCT, so that the Monte Carlo simulation CBCT imaging has better practical application value.
Description of the drawings:
FIG. 1 is a schematic diagram of a cluster in an embodiment;
FIG. 2 is a schematic diagram of a parallel acceleration method in an embodiment.
The specific embodiment is as follows:
the present invention will be described in further detail with reference to the drawings and the technical scheme, in order to make the objects, technical schemes and advantages of the present invention more apparent.
In order to solve the problems of low imaging speed and the like of the existing Monte Carlo simulation CBCT, an embodiment of the invention, as shown in figures 1 and 2, provides a cone beam CT Monte Carlo simulation cluster parallel acceleration method, which comprises the following steps:
s101, in a cluster, a host and each node are configured with an information transfer interface (MPI) for coordinating parallel running states, the states of the host and the nodes connected into the cluster are determined according to an IP list of the information transfer interface (MIP), and each node is numbered;
s102, the host generates a plurality of Monte Carlo program data for cone beam CT simulation, the number of which is consistent with the number of the nodes, and the initial number of sampling particles in each program data is set according to the number of the nodes so as to run the simulation of the corresponding number of particles in the nodes;
s103, in the MIP command mode, the host controls each node to synchronously operate Monte Carlo program data according to the respective sampling particle initial numbers, and particle tracks for recording the distance coordinate characteristics of the detector and the ray source are generated; each node uses a memory mapping mechanism to read all particle track data into a memory, respectively puts the particle tracks into grids according to the size of a set detector grid, counts the number of particles in each grid, and establishes a storage matrix for storing the statistical data of the number of particles;
s104, the host collects the storage matrixes of all the nodes and performs comprehensive accumulation on the storage matrixes to obtain a graph data matrix for drawing the image.
The biggest problem faced by adopting single-machine simulation CBCT imaging is that the simulation speed is low, the FIR card with the MCNP can be used for simulating cone beam X-ray imaging, the simulation time of a single image of the FIR card is too long, the application value of the single image is greatly reduced, and more images are needed for CBCT to finish image reconstruction. The universal Monte Carlo program MCNP realizes the function of tracking the particle track in real time, and outputs the particle track file, namely the PTRAC file, by the track. The data for generating the PTRAC file is fast, and can reach the speed of 20 ten thousand/min. By utilizing the advantages of the computer cluster, the computers in the same scheduling cluster can realize the high-speed Monte Carlo simulation of the CBCT under the support of the MPI. In the embodiment of the invention, under the support of MPI, a plurality of computers are used for realizing parallel acceleration of clusters, so that the speed of processing the Monte Carlo simulation CBCT particle track file is improved; compared with a main-flow single-machine Monte Carlo simulation mode, the cluster mode can fully utilize the advantages of a computer cluster, improve the efficiency and performance of the Monte Carlo simulation CBCT, and enable the Monte Carlo simulation CBCT imaging to have good practical application value.
As the cone beam CT Monte Carlo simulation cluster parallel acceleration method in the embodiment of the invention, further, each computer in the cluster is configured in the same network group, one computer is selected as a host, and the rest computers are used for each node. Further, the particle trajectories are traversed and each particle is projected into a two-dimensional grid to count the grid particle numbers. Further, in the integrated accumulation of the storage matrix, the data of the same position is added, and the gray scale range for displaying the picture is set for CT imaging.
Configuring computers in the cluster into the same network group; according to the number and the performance of the computer clusters, selecting computers with good performance in the clusters as hosts, taking the rest computers as nodes, and creating accounts with administrator rights for all computers in the clusters; configuring MPI in the cluster, ensuring that all computers are normally connected into the cluster according to an IP list of the MPI, and communicating with each other; each node computer is numbered so as to be convenient for the later parallel operation.
Writing an INP file of the MCNP in a host according to the requirement, selecting PTRAC by an output mode in the INP file, setting the writing content of the PTRAC file as POS, and using the setting to reduce the size of the PTRAC file. The data card file is sent to each node and has the same file path. And in the MPI parallel command mode, sending an instruction for starting the MCNP to all nodes, and starting a simulation process according to the generated data card content.
PTRAC files are generated under the current path of each node, the file type is O files, and the extension name is O. The host sends a script for modifying the file name to each node, and uniformly modifies the file type into a TXT format file with the assistance of MPI. The data processing program may be written in c++. And adopting a memory mapping mechanism to map all PTRAC data into the memory at one time. A two-dimensional grid is created from the PTRAC file particle range, and the spacing of the grids is determined according to the resolution of the image. Traversing PTRAC file, projecting each particle into two-dimensional grid, counting the particle number of each grid in the grid, creating a matrix for saving the particle number of each grid, and saving the matrix into the file after completion. Compiling the data processing file into an release version of the exe file. The host sends data processing programs to each node, and the PTRAC files are synchronously processed with the assistance of MPI. And storing the data processing result into a file, and modifying the file name of the file according to the number of each node. And after the data processing is finished, sending the data processing result file of each node to the host. And the host combines the data processing results sent by the nodes, adds the data with the same shape and position to generate a new file, sets a gray scale range according to the requirement, displays pictures and completes CT imaging.
Further, the embodiment of the invention also provides a cone beam CT Monte Carlo simulation cluster parallel acceleration system, which comprises: a host and a plurality of nodes disposed within the cluster, wherein,
the host and each node are configured with an information transfer interface MPI for coordinating parallel running states, the states of the host and the nodes connected into the cluster are determined according to an IP list of the information transfer interface MIP, and each node is numbered;
the host generates a plurality of Monte Carlo program data for cone beam CT simulation, the number of which is consistent with the number of the nodes, and the initial number of sampling particles in each program data is set according to the number of the nodes so as to run the simulation of the corresponding number of particles in the nodes;
in the MIP command mode, the host controls each node to synchronously run Monte Carlo program data according to the respective sampling particle initial numbers, and particle tracks for recording the distance coordinate characteristics of the detector and the ray source are generated; each node uses a memory mapping mechanism to read all particle track data into a memory, respectively puts the particle tracks into grids according to the size of a set detector grid, counts the number of particles in each grid, and establishes a storage matrix for storing the statistical data of the number of particles;
the host collects the storage matrix of each node and performs comprehensive accumulation on the storage matrix to obtain a graph data matrix for drawing the image.
In order to verify the effectiveness of the technical scheme of the invention, the following is further explained by combining with a specific simulation experiment:
taking into consideration that a beam of X-rays is simulated to irradiate into an aluminum column, and counting and tracking the energy and the coordinates of each X-ray photon reaching the detector; the photon number of the sampled X-ray is 4X 108 photons, and the cluster comprises one host computer and 40 nodes; the mesh size of the detector used for imaging is 200 x 200. The specific implementation steps are as follows:
step 1: as shown in FIG. 1, the distributed cluster comprises a host, 40 node computers, node numbers 1-40; the host and the nodes form a local area network through the switch. Firstly, setting an administrator-level account and a password on each node computer, configuring MCNP software under the account, configuring MPI software and setting global variables respectively;
step 2: copying MPI parallel execution files to an MCNP installation directory in each node, generating 40 MCNP data card files in a host, and recording the track of particles in the data card files by using PTRAC. The coordinate characteristics of the particle tracking file are set according to the distance between the detector and the radiation source, and in this example, particles whose coordinates lie in the range of [ -100,100] are set and tracked X, Y according to the size of the detector. Setting the initial number of sampling particles in each data card file to be 1-40 respectively. And respectively transmitting each file to a corresponding node computer MCNP installation catalog according to the initial number of the sampling particles in the host.
Step 3: an MPI window is started in the host, computers in the whole cluster are checked by utilizing a subprogram MPICH Configuration of the MPI, and the communication condition of the nodes and the host is tested. And starting MCNP program simulation programs of all the nodes in an MPI multi-machine command mode, wherein data cards of the simulation programs are used for data card files placed under each node in advance.
Step 4: after all nodes run, a program for processing the PTRAC file is sent from the host to each partition node, and the data processing program uses a memory mapping mechanism to read all data into a memory once, so that the data processing speed can be increased; after the data are read into the memory, the particle tracks are respectively put into grids which are divided into 200 x 200 parts and are evenly divided according to the size of the detector, the number of particles entering each grid is counted, a matrix with the same size is established, the value of each element in the matrix is stored into the counted number of particles, the counted matrix result is stored into a local TXT file, and the file is named the same according to the local file number.
Step 5: and sending an instruction to each node in the host, sending the files processed by the data in 0012 to the host, and adding the numbers at the same position by the host according to the matrix accumulation in the collected files.
Step 6: the final matrix accumulated in step 0013 is displayed in the host in the form of an image, and the value of each grid is considered as its gray value.
Through the experimental steps, the acceleration ratio of cluster calculation in the implementation of the invention is approximately equal to the number of cluster nodes, and communication and data exchange between a host and the nodes can be conveniently realized based on an MPI message transmission mechanism; the problem of low Monte Carlo simulation efficiency can be well solved; the MPI message transmission mechanism is utilized to realize the aim of synchronous operation of different computers under the same network, the mater is responsible for sending commands to each node, coordinating the whole cluster processing data, sending the processing result to the host, and finally the host integrates and displays CT images uniformly the data processed by each node, thereby effectively improving the efficiency and performance of the cone beam CT image reconstruction system.
The relative steps, numerical expressions and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
Based on the above system, the embodiment of the present invention further provides a server, including: one or more processors; and a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the systems or methods described above.
Based on the above system, the embodiments of the present invention further provide a computer readable medium having a computer program stored thereon, wherein the program when executed by a processor implements the above system or method.
The device provided by the embodiment of the present invention has the same implementation principle and technical effects as those of the embodiment of the system, and for the sake of brevity, reference may be made to the corresponding content of the embodiment of the system.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system and apparatus may refer to corresponding procedures in the foregoing system embodiments, which are not described herein again.
Any particular values in all examples shown and described herein are to be construed as merely illustrative and not a limitation, and thus other examples of exemplary embodiments may have different values.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, systems and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and systems may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to execute all or part of the steps of the system according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. The cone beam CT Monte Carlo simulation cluster parallel acceleration method is characterized by comprising the following steps of:
in the cluster, a host and each node are configured with an information transfer interface MPI for coordinating parallel running states, the states of the host and the nodes connected into the cluster are determined according to an IP list of the information transfer interface MIP, and each node is numbered;
the host generates a plurality of Monte Carlo program data for cone beam CT simulation, the number of which is consistent with the number of the nodes, and the initial number of sampling particles in each program data is set according to the number of the nodes so as to run the simulation of the corresponding number of particles in the nodes;
in the MIP command mode, the host controls each node to synchronously run Monte Carlo program data according to the respective sampling particle initial numbers, and particle tracks for recording the distance coordinate characteristics of the detector and the ray source are generated; each node uses a memory mapping mechanism to read all particle track data into a memory, respectively puts the particle tracks into grids according to the size of a set detector grid, counts the number of particles in each grid, and establishes a storage matrix for storing the statistical data of the number of particles;
the host computer collects the storage matrixes of all the nodes and carries out comprehensive accumulation on the storage matrixes to obtain a graph data matrix for drawing the image, wherein in the comprehensive accumulation of the storage matrixes, the data in the same form and position are added, and the gray scale range for displaying the image is set to carry out CT imaging.
2. The cone beam CT monte carlo simulation cluster parallel acceleration method according to claim 1, wherein each computer in the cluster is configured in the same network group, one of the computers is selected as a host, and the rest of the computers are used for each node.
3. The cone beam CT monte carlo simulation cluster parallel acceleration method of claim 1, wherein the particle trajectories are traversed and each particle is projected into a two-dimensional grid to count the grid particle number.
4. A cone beam CT monte carlo simulation cluster parallel acceleration system, comprising: a host and a plurality of nodes disposed within the cluster, wherein,
the host and each node are configured with an information transfer interface MPI for coordinating parallel running states, the states of the host and the nodes connected into the cluster are determined according to an IP list of the information transfer interface MIP, and each node is numbered;
the host generates a plurality of Monte Carlo program data for cone beam CT simulation, the number of which is consistent with the number of the nodes, and the initial number of sampling particles in each program data is set according to the number of the nodes so as to run the simulation of the corresponding number of particles in the nodes;
in the MIP command mode, the host controls each node to synchronously run Monte Carlo program data according to the respective sampling particle initial numbers, and particle tracks for recording the distance coordinate characteristics of the detector and the ray source are generated; each node uses a memory mapping mechanism to read all particle track data into a memory, respectively puts the particle tracks into grids according to the size of a set detector grid, counts the number of particles in each grid, and establishes a storage matrix for storing the statistical data of the number of particles;
the host computer collects the storage matrixes of all the nodes and carries out comprehensive accumulation on the storage matrixes to obtain a graph data matrix for drawing the image, wherein in the comprehensive accumulation of the storage matrixes, the data in the same form and position are added, and the gray scale range for displaying the image is set to carry out CT imaging.
5. A server, comprising: one or more processors; a storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-3.
6. A computer readable medium having stored thereon a computer program to be run by a processor for performing the method of any of claims 1-3.
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