CN116612853A - Radiotherapy verification plan dose generation method, radiotherapy verification plan dose generation system, electronic equipment and storage medium - Google Patents
Radiotherapy verification plan dose generation method, radiotherapy verification plan dose generation system, electronic equipment and storage medium Download PDFInfo
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
- G16H20/17—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
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- G06N3/00—Computing arrangements based on biological models
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Abstract
The invention discloses a method, a system, electronic equipment and a storage medium for generating a radiotherapy verification plan dose, which are used for solving the problem of low efficiency when the existing radiotherapy verification plan carries out Monte Carlo dose calculation. Acquiring a case verification plan data set, and acquiring projection image information of a plan portal through data analysis; taking die body CT image data, die body outline structure data and projection image information corresponding to the case verification plan data set as input data of a deep learning model, taking a dose distribution map as output of the deep learning model, and training the deep learning model until a dose calculation network model is obtained; the dose calculation network model is used for carrying out dose calculation on the case information to be calculated, so that the dose of a patient verification plan can be quickly generated, and the accuracy of the plan verification is improved; the dosage is automatically calculated through the deep learning model, so that the interference of human factors is reduced, the error rate is reduced, and the working efficiency is improved.
Description
Technical Field
The invention belongs to the technical field of radiotherapy dose calculation, and particularly relates to a radiotherapy verification plan dose generation method, a radiotherapy verification plan dose generation system, electronic equipment and a storage medium.
Background
With the progress of computer and automatic control technologies, radiation therapy technology is becoming more and more complex, and requirements for dose verification are also increasing. Dose verification is a key safety management measure in radiotherapy. By verifying the planned and actual measured doses, potential errors or deviations in the treatment plan can be discovered and corrected in time. This helps to reduce the risk in the treatment and ensures that the patient gets maximum safety when receiving radiation therapy.
One of the dose verification methods commonly used in clinical radiotherapy today is the use of detector arrays. The basic procedure of this method is to change the CT image of the patient in the treatment plan to the CT image of the phantom of the detector array and to perform a dose recalculation. The detector array is then irradiated on the accelerator using the treatment plan, and the detectors in the phantom array can measure the dose distribution over a specific spatial range and compare it to the calculated dose distribution to determine if the plan can be validated.
Dose calculations are typically performed in various planning systems, with different algorithms being used by the different planning systems. The dosage algorithms currently in clinical use are: the convolution algorithm has the advantages of high calculation speed, but larger calculation error at boundary positions such as non-uniform tissues and surfaces; the Monte Carlo algorithm, which is a high-precision algorithm simulating particle transport, can take into account various physical processes and material heterogeneities, however, this method requires a lot of computational resources and time; the method is similar to a Monte Carlo algorithm, and the calculation accuracy and the calculation efficiency of the method are between the convolution type algorithm and the Monte Carlo algorithm.
Different dose calculation algorithms differ in calculation accuracy, calculation speed and application range. Dose calculation accuracy has an important impact on the outcome of dose verification. In addition, large medical centers may have multiple accelerator treatment machines of different manufacturer models, and correspondingly may have multiple different planning systems, each using a different dose calculation algorithm. The verification plan is required when performing the verification work of the radiotherapy plan, and a great deal of manual operation is still required when generating the verification plan dose. At present, a dose calculation method with high calculation speed, high precision and high automation degree is particularly needed for dose verification of radiotherapy planning.
Disclosure of Invention
The invention aims to provide a radiotherapy verification plan dose generation method, a radiotherapy verification plan dose generation system, electronic equipment and a storage medium, which are used for solving the problem of low efficiency when the existing radiotherapy verification plan carries out Monte Carlo dose calculation.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the first aspect of the present invention provides a radiotherapy verification plan dose generation method, comprising the steps of:
acquiring a case plan data set comprising radiotherapy verification plan data of a plurality of cases; the radiotherapy verification plan data comprise die body CT image data of cases, verification plan radiation field data and die body outline structure data;
calculating the radiotherapy verification plan data by adopting a Monte Carlo method to obtain a dose distribution map on a voxel;
performing data analysis on verification plan field data, wherein the verification plan comprises a plurality of fields, and each field is provided with a plurality of sub-fields;
the data analysis comprises the following steps: extracting MLC position information of each sub-field in each field, performing flux map conversion on all the sub-field MLC position information of each field to obtain flux map information of each field, and converting the flux map information into projection map information in voxels;
selecting a deep learning model, taking the die body CT image data, the die body contour structure data and the projection image information as input data of the deep learning model, taking a dose distribution diagram on a voxel obtained by Monte Carlo calculation as output of the deep learning model, and training the deep learning model until a dose calculation network model is obtained;
and acquiring the case information to be calculated, and performing dose calculation on the case information to be calculated by using the dose calculation network model so as to acquire the dose distribution information of the verification plan on the die body.
In one possible design, when the flux map information is converted into projection map information, the magnitude of the projection assignment for each voxel point of the verification motif is defined as:
wherein ,D i represent the firstiThe projection assignment of the point voxels,d i expression firstiThe distance of the point voxel from the source of the radiation,F ij represent the firstiPoint voxel and ray source connection and flux map intersection pointjCorresponding flux map values.
In one possible design, the deep-learning network model is a three-dimensional Res-Unet model, a Vgg-Unet model, a Trans-Unet model, or a UNeXt model.
In one possible design, the method of outputting a verification plan of the dose distribution on the phantom further comprises; and converting the dose distribution information into a three-dimensional matrix with required spatial resolution, and deriving the three-dimensional matrix in a preset format.
A second aspect of the invention provides an automatic dose calculation system for radiation therapy verification planning,
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring a case plan data set, and the case plan data set comprises radiotherapy verification plan data of a plurality of cases; the radiotherapy verification plan data comprise die body CT image data of cases, verification plan radiation field data and die body outline structure data; the radiotherapy verification plan data are transmitted to the first computing module, and the verification plan radiation field data are transmitted to the data analysis module; transmitting the die body CT image data and the die body contour structure data to a model training module;
the first calculation module is used for receiving the radiotherapy verification plan data transmitted by the data acquisition module, and calculating the radiotherapy verification plan data by adopting a Monte Carlo method to obtain a dose distribution diagram on a voxel; transmitting the dose distribution map on the voxels to a model training module;
the data analysis module is used for receiving the verification plan field data transmitted by the data acquisition module and carrying out data analysis on the verification plan field data, wherein the verification plan comprises a plurality of fields, and each field is provided with a plurality of sub-fields; the data analysis comprises the following steps: extracting MLC position information of each sub-field in each field, performing flux map conversion on all the sub-field MLC position information of each field to obtain flux map information of each field, and converting the flux map into projection map information in voxels; transmitting the projection map information to a model training module;
the model training module is used for receiving the die body CT image data and the die body contour structure data transmitted by the data acquisition module, receiving the dose distribution on the voxels transmitted by the first calculation module, receiving the projection image information transmitted by the data analysis module, then taking the die body CT image data, the die body contour structure data and the projection image information as input data of a deep learning model, calculating the dose distribution map on the voxels by Monte Carlo to obtain the output of the deep learning model, and training the deep learning model until a dose calculation network model is obtained;
the dose calculation module is used for acquiring the case information to be calculated, and performing dose calculation on the case information to be calculated by using the dose calculation network model acquired by the model training module so as to acquire the dose distribution information of the verification plan on the die body.
In one possible design, a dose output module is also included,
the dose calculation module also transmits the obtained dose distribution information of the verification plan on the die body to the dose output module; the dose output module is used for converting the dose distribution information into a three-dimensional matrix with required spatial resolution, and deriving the three-dimensional matrix in a preset format.
A third aspect of the present invention provides an electronic device, including a memory and a processor, where the memory and the processor are connected to each other by a bus; the memory stores computer-executable instructions; the processor executes computer-executable instructions stored in the memory, causing the processor to perform the radiotherapy verification plan dose generation method as described in the first aspect or any one of the possible designs of the first aspect.
A fourth aspect of the invention provides a storage medium having instructions stored thereon which, when run on a computer, perform a radiotherapy verification plan dose generation method as described in the first aspect or any of the possible designs of the first aspect.
The beneficial effects are that: according to the invention, the dose of the patient verification plan can be quickly generated by establishing the dose calculation network model, so that the accuracy of the plan verification is improved; the dosage is automatically calculated through the deep learning model, so that the interference of human factors is reduced, the error rate is reduced, the workload of medical staff is reduced, and the working efficiency is improved; the invention can perform unified calculation and data management on verification plans of different planning systems, and is convenient for subsequent data analysis and research.
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FIG. 1 is a flow chart diagram of a radiotherapy verification plan dose generation method according to an embodiment of the present invention;
fig. 2 is a block diagram of a radiotherapy verification plan dose generation system according to an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly described below with reference to the accompanying drawings and the description of the embodiments or the prior art, and it is obvious that the following description of the structure of the drawings is only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art. It should be noted that the description of these examples is for aiding in understanding the present invention, but is not intended to limit the present invention.
Examples:
as shown in fig. 1, a first aspect of the present embodiment provides a radiotherapy verification plan dose generating method, including the steps of:
acquiring a case plan data set comprising radiotherapy verification plan data of a plurality of cases; the radiotherapy verification plan data comprise die body CT image data of cases, verification plan radiation field data and die body outline structure data;
calculating the radiotherapy verification plan data by adopting a Monte Carlo method to obtain a dose distribution map on a voxel;
performing data analysis on verification plan field data, wherein the verification plan comprises a plurality of fields, and each field is provided with a plurality of sub-fields;
the data analysis comprises the following steps: extracting the positions of multi-leaf collimators (MultiLeaf Collimator, MLC) of each sub-field in each field, performing flux map conversion on all sub-field MLC position information of each field to obtain flux map information of each field, and converting the flux map information into projection map information in voxels;
selecting a deep learning model, taking the die body CT image data, the die body contour structure data and the projection image information as input data of the deep learning model, taking a dose distribution diagram on a voxel obtained by Monte Carlo calculation as output of the deep learning model, and training the deep learning model until a dose calculation network model is obtained;
and acquiring the case information to be calculated, and performing dose calculation on the case information to be calculated by using the dose calculation network model so as to acquire the dose distribution information of the verification plan on the die body.
In a specific example, the verification plan is a fixed field intensity modulated radiation therapy plan, the plan including six fields, each field having a plurality of sub-fields. The verification plan may also be a volume-rotation intensity modulated radiation therapy plan, which includes two fields, each field having 91 control points.
In one possible implementation, when the flux map information is converted into projection map information, the magnitude of the projection assignment for each voxel point of the verification motif is defined as:
wherein ,D i represent the firstiThe projection assignment of the point voxels,d i expression firstiThe distance of the point voxel from the source of the radiation,F ij represent the firstiPoint voxel and ray source connection and flux map intersection pointjCorresponding flux map values.
In one possible implementation, the deep learning network model is a three-dimensional Res-Unet model, a Vgg-Unet model, a Trans-Unet model, or a UNeXt model.
In one possible embodiment, the method of outputting a verification plan of the dose distribution on the phantom further comprises; and converting the dose distribution information into a three-dimensional matrix with required spatial resolution, and deriving the three-dimensional matrix in a preset format. Specifically, the preset format is the format of a DICOM file.
As shown in fig. 2, a second aspect of the present invention provides an automated dose calculation system for radiation therapy verification planning,
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring a case plan data set, and the case plan data set comprises radiotherapy verification plan data of a plurality of cases; the radiotherapy verification plan data comprise die body CT image data of cases, verification plan radiation field data and die body outline structure data; transmitting the radiotherapy verification plan data to a first computing module, and transmitting verification plan radiation field data to a data analyzing module; transmitting the die body CT image data and the die body contour structure data to a model training module;
the first calculation module is used for receiving the radiotherapy verification plan data transmitted by the data acquisition module, and calculating the radiotherapy verification plan data by adopting a Monte Carlo method to obtain a dose distribution diagram on a voxel; transmitting the dose distribution map on the voxels to a model training module;
the data analysis module is used for receiving the verification plan field data transmitted by the data acquisition module and carrying out data analysis on the verification plan field data, wherein the verification plan comprises a plurality of fields, and each field is provided with a plurality of sub-fields; the data analysis comprises the following steps: extracting MLC position information of each sub-field in each field, performing flux map conversion on all the sub-field MLC position information of each field to obtain flux map information of each field, and converting the flux map into projection map information in voxels; transmitting the projection map information to a model training module;
the model training module is used for receiving the die body CT image data and the die body contour structure data transmitted by the data acquisition module, receiving the dose distribution on the voxels transmitted by the first calculation module, receiving the projection image information transmitted by the data analysis module, then taking the die body CT image data, the die body contour structure data and the projection image information as input data of a deep learning model, calculating the dose distribution map on the voxels by Monte Carlo to obtain the output of the deep learning model, and training the deep learning model until a dose calculation network model is obtained;
the dose calculation module is used for acquiring the case information to be calculated, and performing dose calculation on the case information to be calculated by using the dose calculation network model acquired by the model training module so as to acquire the dose distribution information of the verification plan on the die body.
In one possible embodiment, the device further comprises a dose output module,
the dose calculation module also transmits the obtained dose distribution information of the verification plan on the die body to the dose output module; the dose output module is used for converting the dose distribution information into a three-dimensional matrix with required spatial resolution, and deriving the three-dimensional matrix in a preset format.
A third aspect of the present invention provides an electronic device, including a memory and a processor, where the memory and the processor are connected to each other by a bus; the memory stores computer-executable instructions; the processor executes computer-executable instructions stored in the memory, causing the processor to perform the radiotherapy verification plan dose generation method as described in the first aspect or any of the possible implementation manners of the first aspect. By way of specific example, the Memory may include, but is not limited to, random-Access Memory (RAM), read-Only Memory (ROM), flash Memory (Flash Memory), first-in first-out Memory (First Input First Output, FIFO), and/or first-in last-out Memory (First Input Last Output, FILO), etc.; the processor may not be limited to use with a processor of the type X86, internet series or other microprocessor; the transceiver may be, but is not limited to, a wired transceiver, a wireless fidelity (Wireless Fidelity, wiFi) wireless transceiver, a bluetooth wireless transceiver, a general packet radio service technology (General Packet Radio Service, GPRS) wireless transceiver, and/or a ZigBee wireless transceiver (low power local area network protocol based on the ieee802.15.4 standard), etc. The electronic device may also include, but is not limited to, a power module, a display screen, and other necessary components.
A fourth aspect of the invention provides a storage medium having instructions stored thereon which, when run on a computer, perform a radiotherapy verification plan dose generation method as described in the first aspect or any of the possible implementation manners of the first aspect. The computer readable storage medium refers to a carrier for storing data, and may include, but is not limited to, a floppy disk, an optical disk, a hard disk, a flash Memory, and/or a Memory Stick (Memory Stick), etc., where the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable devices.
The working process, working details and technical effects of the foregoing computer readable storage medium provided in the fourth aspect of the present embodiment may be referred to the interaction method described in the foregoing first aspect or any one of possible implementation manners of the first aspect, which are not described herein again.
A fifth aspect of the present embodiment provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform a radiotherapy verification plan dose generation method as described in the first aspect or any one of the possible implementations of the first aspect. Wherein the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus.
Finally, it should be noted that: the foregoing description is only of the preferred embodiments of the invention and is not intended to limit the scope of the invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A method of generating a radiation therapy verification plan dose, comprising the steps of:
acquiring a case plan data set comprising radiotherapy verification plan data of a plurality of cases; the radiotherapy verification plan data comprise die body CT image data of cases, verification plan radiation field data and die body outline structure data;
calculating the radiotherapy verification plan data by adopting a Monte Carlo method to obtain a dose distribution map on a voxel;
performing data analysis on verification plan field data, wherein the verification plan field data comprises a plurality of fields, and each field is provided with a plurality of sub-fields; the data analysis of the verification plan portal data comprises the following steps: extracting MLC position information of each sub-field in each field, performing flux map conversion on all the sub-field MLC position information of each field to obtain flux map information of each field, and converting the flux map information into projection map information in voxels;
selecting a deep learning model, taking the die body CT image data, the die body contour structure data and the projection image information as input data of the deep learning model, taking a dose distribution diagram on a voxel obtained by Monte Carlo calculation as output of the deep learning model, and training the deep learning model until a dose calculation network model is obtained;
and acquiring the case information to be calculated, performing dose calculation on the case information to be calculated by using the dose calculation network model so as to acquire the dose distribution information of the verification plan on the die body, and then outputting the dose distribution information of the verification plan on the die body.
2. The radiation therapy verification plan dose generating method according to claim 1, wherein when the flux map information is converted into projection map information in voxels, projection assignment is performed on each voxel point of the corresponding verification phantom, and the size of the projection assignment is defined as:
wherein ,D i represent the firstiThe projection assignment of the point voxels,d i expression firstiThe distance of the point voxel from the source of the radiation,F ij represent the firstiPoint voxel and ray source connection and flux map intersection pointjCorresponding flux map values.
3. The method of generating a radiation therapy validated plan dose of claim 1,
the deep learning network model is a three-dimensional Res-Unet model, a Vgg-Unet model, a Trans-Unet model or a uneXt model.
4. The method of generating a radiation therapy validated plan dose of claim 1,
the method of outputting a dose distribution of the verification plan on the phantom further comprises; and converting the dose distribution information into a three-dimensional matrix with required spatial resolution, and deriving the three-dimensional matrix in a preset format.
5. An automatic dose calculation system for a radiation treatment verification plan, characterized in that,
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring a case plan data set, and the case plan data set comprises radiotherapy verification plan data of a plurality of cases; the radiotherapy verification plan data comprise die body CT image data of cases, verification plan radiation field data and die body outline structure data; transmitting the radiotherapy verification plan data to a first computing module, and transmitting verification plan radiation field data to a data analyzing module; transmitting the die body CT image data and the die body contour structure data to a model training module;
the first calculation module is used for receiving the radiotherapy verification plan data transmitted by the data acquisition module, and calculating the radiotherapy verification plan data by adopting a Monte Carlo method to obtain a dose distribution diagram on a voxel; transmitting the dose distribution map on the voxels to a model training module;
the data analysis module is used for receiving the verification plan field data transmitted by the data acquisition module and carrying out data analysis on the verification plan field data, wherein the verification plan field comprises a plurality of fields, and each field is provided with a plurality of sub-fields; the data analysis comprises the following steps: extracting MLC position information of each sub-field in each field, performing flux map conversion on all the sub-field MLC position information of each field to obtain flux map information of each field, and converting the flux map into projection map information in voxels; transmitting the projection map information to a model training module;
the model training module is used for receiving the die body CT image data and the die body contour structure data transmitted by the data acquisition module, receiving the dose distribution on the voxels transmitted by the first calculation module, receiving the projection image information transmitted by the data analysis module, then taking the die body CT image data, the die body contour structure data and the projection image information as input data of a deep learning model, calculating the dose distribution map on the voxels by Monte Carlo to obtain the output of the deep learning model, and training the deep learning model until a dose calculation network model is obtained;
the dose calculation module is used for acquiring the case information to be calculated, and performing dose calculation on the case information to be calculated by using the dose calculation network model acquired by the model training module so as to acquire the dose distribution information of the verification plan on the die body.
6. The automated radiation therapy verification plan dose calculation system of claim 5, further comprising a dose output module,
the dose calculation module also transmits the obtained dose distribution information of the verification plan on the die body to the dose output module; the dose output module is used for converting the dose distribution information into a three-dimensional matrix with required spatial resolution, and deriving the three-dimensional matrix in a preset format.
7. An electronic device, comprising a memory and a processor, wherein the memory and the processor are connected with each other through a bus; the memory stores computer-executable instructions; the processor executing computer-executable instructions stored in a memory causes the processor to perform the radiotherapy verification plan dose generation method of any one of claims 1 to 4.
8. A storage medium having instructions stored thereon which, when executed on a computer, perform the radiation therapy verification plan dose generation method of any one of claims 1 to 4.
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