132 results about "Radiotherapy dose" patented technology

The invention discloses a tumor radiotherapy dose calibration method, and relates to the technical field of tumor radiotherapy, and the method comprises the steps: collecting multi-modal data in real time through 4D-CT, MRI and the like, extracting dynamic features through space-time registration, calculating a risk organ dose ratio and a tumor target dose deviation rate, and constructing a comprehensive evaluation function. The method has the advantages that the multi-modal dynamic data of the tumor target area are synchronously collected in real time, the space-time joint registration algorithm and the time sequence weight factor are combined, the limitation that existing static calibration depends on fixed parameters is broken through, and the method is suitable for the multi-modal dynamic data of the tumor target area. According to the method, 95% dose coverage of a tumor target region is guaranteed, and meanwhile, organ-endangering peak value receiving amount is controlled within a certain range of a clinical tolerance threshold value, so that the risk of excessive irradiation of normal tissues is reduced, and the radiotherapy dose calibration precision is prompted based on the deviation rate of a traditional method.

The invention discloses a self-adaptive aid decision-making method, system and device for patient radiotherapy, and a medium, and relates to the field of smart medical treatment. The method comprises the following steps: standardizing and preprocessing multi-source original data; performing automatic segmentation and change detection on tumor and normal tissues based on the processed data; delineating a tumor target region and a normal tissue in radiotherapy in a detection result; generating radiation therapy dose distribution according to the sketching result; evaluating the radiation treatment dose distribution by adopting an optimization evaluation method to generate a plurality of alternative treatment schemes; determining an optimal treatment regimen based on the plurality of alternative treatment regimens; and performing radiotherapy based on the optimal treatment scheme, monitoring anatomical changes of the patient in real time in the radiotherapy process, and dynamically adjusting the optimal treatment scheme. According to the invention, a plurality of operations of patient radiotherapy can be effectively integrated, and precision and individuation of patient radiotherapy can be realized, so that complex changes in the treatment process can be better coped with, and the overall treatment effect can be improved.

The invention belongs to the technical field of radiotherapy dose simulation measurement, and provides a heavy ion fixed machine head radiotherapy dose simulation measurement method and system. The method comprises the following steps: firstly, in a solid water model, according to a layering and key area encryption sampling principle, carrying out finite point and / or cutting layer dose measurement to obtain sparse dose data and a sampling position; inputting the three-dimensional dose distribution and a sampling position mask into a coding-decoding structure and a mask perception neural network with an attention mechanism in a decoding stage, and outputting complete three-dimensional dose distribution; and finally, outputting a simulation result containing dose distribution and uncertainty distribution. The method improves dose reconstruction precision and clinical quality control efficiency, and is suitable for dose verification before heavy ion radiotherapy.

The invention relates to the field of navigation signal processing, and discloses a radiation therapy bolus dose feedback and tracking system based on image registration, the system comprises an image acquisition module, an image registration module, a bolus identification module, a dose recalculation module, a dose deviation analysis module and a feedback and adjustment module, space registration is carried out through an FBCT image acquired every day and an original plan CT, and the radiation therapy bolus dose feedback and tracking system based on image registration is obtained. The fitting state between the bolus and the skin is automatically identified, the position of an air gap is detected, and dose recalculation is carried out based on an actual structure. Through comparison with an original planned dose, the system can output a deviation index of a key dose parameter and provide visual feedback, if the deviation exceeds a set threshold value, a prompt is automatically sent out, and a radiotherapy technician and a physician are supported to carry out bolus adjustment or call an adaptive planning engine to reconstruct a treatment plan. Quantitative analysis of bolus fitting quality and closed-loop control of dose delivery consistency are realized, radiotherapy precision and safety are effectively improved, and the method is suitable for superficial radiotherapy scenes containing bolus design.

The invention relates to the technical field of nasopharyngeal carcinoma radiotherapy, in particular to a nasopharyngeal carcinoma radiotherapy dose automatic prediction method based on deep learning, and the method comprises the following steps: S1, analyzing obtained original data; s2, writing a program to pre-process all the original data in batches; s3, calculating a corresponding dose volume histogram according to the anatomical contour and the patient dose distribution; and S4, inputting the preprocessed data into a designed deep learning model for dose distribution training and prediction, and comparing with an existing dose prediction method to verify the innovativeness and feasibility of the scheme. Dose prediction is automatically carried out through the deep learning model, manual rule design is not needed, high-quality dose distribution can be rapidly generated, batch preprocessing is carried out on all original data, dose distribution training and prediction are carried out by inputting the data into the designed deep learning model, the calculation performance is improved, and the method is suitable for large-scale popularization and application. And the training and reasoning efficiency is improved.

The invention discloses a radiotherapy dose calculation optimization method and device based on a generative adversarial network, and relates to the technical field of medical plans, and the method comprises the steps: obtaining phase space files and condition vectors of different accelerators, and making a data set; training and verifying the constructed generative adversarial network model by using the data set; determining the value of each parameter in the condition vector according to the actual operation parameter of the specific accelerator, inputting the value to the verified generative adversarial network model, and generating particle distribution data corresponding to the condition vector; and taking particle distribution data generated by the model as initial input of a Monte Carlo (MC) simulation method to obtain a radiotherapy dose distribution result simulated by the MC. Radiotherapy dose calculation is realized through a method of combining the generative adversarial network and MC simulation, and the duration of MC simulation and quality inspection processes can be greatly shortened while the dose calculation precision is ensured.

An external beam radiation therapy dose prediction system through a latent diffusion model is adapted to predict an external beam radiation therapy plan according to a clinical data of a patient and includes a storage unit adapted to store a training data and the clinical data and a processor signally connected to the storage unit. The training data includes a plurality of medical images, a plurality of dose distribution data, and a plurality of training prompts. The processor is adapted to input the training data to a latent diffusion training model to execute a latent diffusion model training and generate an external beam radiation therapy plan model and input the clinical data and at least one prompt to the external beam radiation therapy plan model to generate an external beam radiation therapy plan.

An automated adaptive radiotherapy system based on machine learning for designing and adapting personalized dosing plans and a corresponding system, the system comprising: • a data acquisition module to collect multimodal patient data such as anatomical images, genomics, physiological signals and electronic health records; • a preprocessing department that performs the normalization, alignment, segmentation and transformation of the acquired data into data structures suitable for predictive modeling; • a dose prediction engine containing at least one machine learning model trained to predict personalized three-dimensional radiotherapy dose distributions and dose-volume histograms from the preprocessed data; • an adaptation module to receive updated clinical data and automatically adapt the dosing schedule to intra-fraction and inter-fraction changes in patient anatomy and response to treatment; • a clinician dashboard that displays predicted dosing schedules, allows clinician interaction, and can make the model interpretable through explainable AI; • a data security and compliance layer that protects data through encryption, access control, and regulatory compliance.

The invention discloses a deep learning model for radiotherapy dose distribution prediction and a radiotherapy plan generation method, and the deep learning model is constructed based on a UNet model, is obtained by inputting a training data set into the UNet model for training, and comprises a coding part which is composed of a mixing module and a down-sampling layer and is used for extracting data features, and the down-sampling layer uses maxpoo; the decoding part is composed of a decoding layer mixing module and an up-sampling layer and is used for gradually recovering the size of the original resolution, and the up-sampling layer adopts transposition convolution; the jump connection part is used for connecting the encoding part and the decoding part, and extracting features which are beneficial for the decoding part to recover to the original resolution from the encoding part; wherein the mixing module is composed of a plurality of branch paths, and outputs of the plurality of branch paths are added to serve as final output. The deep learning model is constructed by using a multi-branch path combination strategy, three-dimensional dose distribution can be quickly and accurately predicted, and plan generation in radiotherapy can be quickly completed.

Provides is a method for determining a radiotherapy dose. The method includes: acquiring a plurality of voxels by performing a three-dimensional meshing on a simulation region; acquiring simulation results of a plurality of particles by performing a parallel Monte Carlo simulation on the plurality of particles sampled within the simulation region; and determining a deposition dose for each of the plurality of voxels based on the simulation results of the plurality of particles.

The invention discloses a three-mode radiotherapy dose real-time monitoring system, and belongs to the technical field of medical instruments. The system comprises a contour scanning imaging subsystem used for collecting body surface three-dimensional information of a monitoring target so as to accurately position an affected part; the Cerenkov imaging subsystem is used for collecting Cerenkov light in the radiotherapy process, quantitatively analyzing the relationship between the Cerenkov light intensity and the radiotherapy planning dose, and verifying the accuracy of the radiotherapy dose in real time; the near-infrared imaging subsystem is used for acquiring a near-infrared image; the near-infrared imaging module is used for adding a near-infrared image dimension to an original contour three-dimensional image to form a new multi-dimensional image so as to weaken a registration sliding effect and accurately position an affected part; the method is also used for extracting a blood vessel image so as to remove blood vessels on the Cherenkov image, and blood vessel interference is avoided. According to the invention, through three-mode imaging fusion, dimension raising can be carried out on the three-dimensional contour image so as to more accurately position an affected part, interference of blood vessels on Cerenkov imaging is eliminated, and precision is improved.

A neural network is trained using a training corpus having a plurality of information features, each of the information features including both a reference radiation treatment dose and at least one corresponding post-treatment patient datum. By one approach, the at least one corresponding post-treatment patient datum comprises patient imagery such as, but not limited to, one or more computed tomography images. That trained neural network facilitates radiation treatment planning by generating resultant treatment efficacy probability information and resultant treatment complications probability information.

The invention relates to the technical field of radiation dose calculation, in particular to a radiation dose calculation method and device based on a V-Net network. The method comprises the following steps: acquiring a CT image and light beam flux information for a focus area of a patient to be treated, wherein the light beam flux information represents parameter information of a light beam for performing radiotherapy on the patient to be treated; and on the basis of the CT image and the beam flux information, through a pre-trained V-Net network, performing radiotherapy dose calculation for a focus area of a patient to be treated, and generating a dose distribution diagram. According to the method, the dose distribution diagram can be automatically generated, human intervention is reduced, and the clinical working efficiency and the adaptability of a treatment plan are improved. Moreover, the pre-trained V-Net network can effectively capture features in the CT image, the calculation speed is greatly improved, the precision of the generated dose distribution diagram is equivalent to the calculation precision of the Monte Carlo method, and both the speed and the precision are taken into account.

The invention discloses a brain tumor disease treatment effect evaluation method, and belongs to the technical field of radiotherapy plan evaluation, and the method specifically comprises the steps: obtaining the brain multi-modal image data of a patient, and extracting a three-dimensional tumor region and a corresponding perfusion parameter; constructing a biological target region hierarchical structure containing a tumor overall region and an anoxic subregion; a relative hypoxia degree parameter is obtained by calculating the cerebral blood flow ratio of the hypoxia subregion to other regions of the tumor; historical treatment case data are deconstructed, and a dose-curative effect associated parameter set is established; training and outputting a dose adjustment strategy model for the hypoxia subregion; and generating a final radiotherapy dose planning scheme according to a dose adjustment value output by the model in combination with a clinical guide basic dose. Through a data-driven dose decision-making mechanism, the limitation of traditional uniform dose irradiation is overcome, so that an objective and reliable evaluation basis is provided for clinical selection and optimization of personalized treatment schemes.

The invention relates to the technical field of radiotherapy dose analysis, in particular to a radioactive cardiotoxicity prediction system based on multi-modal dynamic fusion and a quantitative evaluation method. According to the system, medical images, radiotherapy plans and clinical parameters are acquired through the multi-modal data acquisition module; the image preprocessing and three-dimensional reconstruction module processes the image data and reconstructs a heart model containing a substructure; the dynamic dose module is combined with the four-dimensional respiratory gating image and the dose matrix to generate time sequence dose distribution; the feature fusion and prediction module extracts multi-modal features, performs calculation by using a graph convolutional network, a long-short term memory network and an attention mechanism, and outputs a cardiotoxicity risk quantitative index; and finally generating a heart substructure level risk thermodynamic diagram and a structured report. According to the method, dynamic dose modeling, heart substructure level refined analysis, multi-source information fusion and individualized prediction are realized, and the accuracy, interpretability and clinical auxiliary decision-making value of cardiac toxicity risk assessment are remarkably improved.

The invention provides a dynamic trajectory intensity modulated radiotherapy plan optimization method and system, and belongs to the technical field of radiotherapy equipment. A patient data management module is used for acquiring basic information, patient image data and patient radiotherapy plan data of a patient; the region-of-interest sketching module is used for determining a radiotherapy region-of-interest; the conventional plan design module is used for calculating a radiation physical dose according to the conventional plan parameters and performing reverse optimization of a conventional dose rate; and the dynamic track intensity-modulated radiotherapy plan design module is used for calculating the intensity-modulated radiation physical dose according to the dynamic track intensity-modulated radiotherapy plan parameters, and performing dynamic track intensity-modulated radiotherapy dose reverse optimization. According to the method, the optimization speed and quality of the dynamic trajectory intensity modulated radiation therapy plan are improved by using an advanced motion planning algorithm in automatic driving, and the quality of the radiation therapy plan can be improved without hardware modification; the blank of the dynamic trajectory intensity modulated radiotherapy technology is filled, and the cancer treatment level is improved.

The invention discloses a radiotherapy sensitivity and toxic and side effect detection system based on multiple omics, and relates to the technical field of radiotherapy, and the system comprises the steps: obtaining genomics data, proteomics data and metabonomics data of a patient at different stages, and generating a patient multi-omics time series data set; based on the patient multi-omics time sequence data set, extracting patient multi-omics time sequence features, and constructing a patient radiotherapy sensitivity dynamic prediction model; combining clinical manifestation and treatment history in clinical data of the patient, generating an adversarial network by utilizing a cGAN condition, generating toxicity simulation data under different radiotherapy doses, and establishing a patient toxic and side effect risk prediction model; and according to the patient radiotherapy sensitivity dynamic prediction model and the toxic and side effect risk prediction model, obtaining an optimal radiotherapy dose interval of the patient, and generating a patient personalized radiotherapy digital twinning scheme. The method has the beneficial effects that the treatment safety and effect of patients are improved, and the method has higher clinical application value and personalized treatment potential.

The invention belongs to the technical field of radiooncology, artificial intelligence and medical image fusion, and particularly relates to a model construction method and system for predicting the radiation pneumonitis risk of a patient receiving immune combined radiotherapy. According to the method, for a non-small cell lung cancer patient receiving immune checkpoint inhibitor (ICIs) combined radiotherapy, clinical information and radiotherapy dose parameters of the patient are collected, a positioning CT image and dynamic immune factor data are simulated, multi-source features including radiomics features, an immune change curve and dose volume indexes are extracted, and a multi-modal fusion prediction model is constructed. The system integrates automatic data input, feature extraction, model training and risk output modules, can output individual radiation pneumonitis risk scores, provides risk grading and dose intervention suggestions, and has high prediction accuracy and clinical interpretability. The method can be widely applied to individualized risk management and precise treatment of non-small cell lung cancer immune combined radiotherapy patients.

The invention provides a radioactive source personal dose real-time monitoring device and a radioactive source personal dose real-time monitoring method based on a double-range self-adaptive mechanism. The radioactive source personal dose real-time monitoring device and method are suitable for intraoperative individual dose collection and dynamic evaluation in the tumor radiotherapy process. The device comprises a flexible attaching substrate, a first measuring range detection sub-module, a second measuring range detection sub-module, a self-adaptive scheduling unit, a temperature compensation module, a breathing synchronous correction module and a wireless communication module. The device can respond to different dose intensities in real time and adaptively switch or fuse channels; the collected dose projection is restored to a planned coordinate system through a breathing phase recognition and target region drift model, and dynamic target region dose tracking is achieved; temperature correction and wireless transmission functions are combined, and intraoperative data visualization and postoperative evaluation are supported. According to the invention, the precision and safety of radiotherapy dose monitoring are improved, and the method is especially suitable for radiotherapy management of areas with significant respiration-induced drift or adjacent key organs.

The invention discloses an AI auxiliary radiotherapy dose intelligent optimization device, which comprises a multi-modal image fusion module, a dose distribution prediction module, a dynamic adaptive optimization module and a prognosis model integration module, and is characterized in that the multi-modal image fusion module is used for aligning anatomical features of different images, eliminating respiratory motion artifacts and predicting the dose distribution of the different images; the dose distribution prediction module is used for rapidly predicting radiotherapy dose distribution based on anatomical features and historical data, the dynamic adaptive optimization module is used for monitoring anatomical changes in real time and dynamically adjusting a dose plan, and the prognosis model integration module is used for quantifying correlation between dose distribution and radioactive injury risks. High-precision alignment and respiratory motion artifact elimination of an anatomical structure are achieved through the multi-modal image fusion module, three-dimensional dose calculation is rapidly and accurately conducted through the dose distribution prediction module, organ displacement and deformation are effectively coped with through the dynamic self-adaptive optimization module through a real-time image monitoring and reinforcement learning algorithm, and the accuracy of the three-dimensional dose calculation is improved. And the prognosis model integration module constructs an individualized risk prediction model.

The invention relates to the technical field of medical artificial intelligence, and provides a radiotherapy decision support system based on genetic characteristics of tumors, and the system comprises a data collection module which collects clinical data and tumor tissue samples of patients, and obtains gene mutation data and gene expression data; the data preprocessing module performs quality evaluation, duplicate removal and cleaning on the data and integrates the data into a data set; an XGBoost algorithm is adopted to screen out key features related to radiotherapy sensitivity from the cleaned data set; inputting the training set into an LSTM model combined with an attention mechanism for model training to obtain the prediction model, and performing training and parameter adjustment on the model; and the input and output module takes the collected data as input and outputs recommended radiotherapy dose. The system integrates genetic features and clinical features of patients, predicts the radiotherapy dose through a machine learning model, provides a personalized radiotherapy scheme for each patient, and improves the treatment effect.

The invention discloses an X-ray thermoacoustic image reconstruction method based on self-supervised multi-view fusion, and relates to the technical field of medical imaging and radiotherapy dose monitoring. The method comprises the following steps: firstly, acquiring initial sound pressure distribution after radiotherapy simulation by utilizing a tissue HU value and related physical attributes, and simulating X-ray induced ultrasonic signals under different sampling conditions under a K-Wave platform so as to construct a diversified training data set, further, providing a multi-view fusion and artifact decoupling network model MFDRNet based on self-supervision, according to the model, sparse acoustic signals and image feature information can be extracted and fused at the same time, reconstructed global and local detail expressions are enhanced through a multi-scale attention mechanism, and separation of a real structure and artifact components is achieved by means of an artifact decoupling structure; in the training process, a self-supervision loss mechanism is adopted, and stable training can still be completed under the condition of lack of high-quality full-sampling labels.

The invention discloses a tumor radiotherapy dose prediction method based on a diffusion model. The implementation scheme is as follows: 1) data collection; 2) data preprocessing; 3) constructing a dose prediction model; 4) training a prediction model; and 5) dose prediction. According to the dose prediction model constructed by the invention, a three-branch encoder is adopted to efficiently extract key features of input information, and accurate fusion of multi-source input features is realized by means of a feature fusion module MFF; the designed full-scale jump connection based on deformable convolution can make full use of the feature distribution of input conditions on different scales, thereby significantly improving the accuracy and robustness of dose prediction. The method can quickly generate a dose distribution result rich in high-frequency information, and has important scientific value and application prospect in the field of precise tumor radiotherapy.

The invention relates to the technical field of medical image processing and radiotherapy, in particular to an OVH-based breast cancer automatic radiotherapy plan design method and system. Constructing a historical database, and integrating the image data of the patient, the delineation structure of the target region and the organ at risk and the dose information; calculating an OVH index, and quantifying a spatial geometrical relationship between the breast cancer tumor target region and the key endangered organ; and extracting a target area external expansion distance Lx corresponding to the specific volume x of the organ in the OVH curve and a dose value Dx corresponding to the volume point in the dose-volume histogram. And constructing a function model between Lx and Dx through linear regression analysis. And for a new patient, the system calculates the Lx value of the organ of interest of the new patient, inputs the value into the linear model, and predicts the corresponding radiotherapy dose Dx. According to the method provided by the invention, intelligent aided design of the breast cancer radiotherapy plan can be realized, and a scientific and acceptable dose prediction basis is provided.

The invention discloses a three-dimensional in-vivo radiotherapy dose monitoring method and system, and the method comprises the steps: collecting an image sequence {In} containing time dimensions at a theta t time interval through CBCT scanning equipment in a radiotherapy process; calculating target area displacement of adjacent frames of images in the image sequence {In} by using a bidirectional optical flow estimation method; state estimation and prediction are carried out on the displacement data based on an adaptive Kalman filtering algorithm, a dynamic displacement rule of the target area in the respiratory cycle is generated, and a respiratory movement prediction result is output; acquiring radiotherapy plan parameters from a radiotherapy plan system, and calculating planned dose distribution without considering respiratory movement; dynamically adjusting a ray transmission path and energy deposition according to a respiratory movement prediction result, and calculating real-time dose distribution of each respiratory time phase; mapping the dose distribution of each time phase to a reference time phase coordinate system to generate accumulated dose distribution; and comparing and verifying the accumulated dose distribution and the planned dose distribution.

The utility model relates to the field of quality control of medical electron linear accelerators. Multi-dimensional radiotherapy dose measuring equipment comprises a multifunctional water tank, mounting grooves arranged in a matrix mode are formed in a bottom plate of the multifunctional water tank, the mounting grooves are used for placing thermoluminescence dosimeters, and film slots used for inserting films are formed in the bottom of the inner wall of the multifunctional water tank; three mounting holes with different heights are formed in a side plate of the multifunctional water tank, the mounting holes are used for being in butt joint with an ionization chamber plug or a water tank plug, and the water tank plug is used for blocking the mounting holes; the ionization chamber plug is used for installing the ionization chamber, and a through hole for inserting the ionization chamber is formed in the ionization chamber plug; the thermoluminescence dosimeter box further comprises a bearing bottom plate, bearing grooves distributed in a matrix mode are formed in the bearing bottom plate, and the bearing grooves are used for containing thermoluminescence dosimeters. By optimizing the structure of the water tank, compared with a traditional one-dimensional water tank, the dose measurement dimensionality is greatly improved.

The present invention relates to the field of clinical radiotherapy technology, and in particular to a test system and method for the effect of an iodized oil area on photon radiotherapy dose. The system comprises: a plurality of iodized oil samples, each of which is made by mixing minced pig liver and iodized oil in different proportions; a cylindrical sample tube, each of which is composed of a plurality of detachably connected sample chambers, each of which is provided with the iodized oil samples in the sample chambers, each corresponding to the iodized oil sample. The linear accelerator irradiates an abdominal simulation phantom into which the sample tube is inserted. The CT scanner is connected to a radiotherapy planning system, which simulates the irradiation of the sample tube with a photon beam based on a CT image and obtains a simulated dose of electron density. The present application can characterize the effect of the iodized oil area on the photon radiotherapy dose by comparing the simulated dose of electron density obtained by the radiotherapy planning system with the actual dose of electron density obtained by an ionization chamber detector.

The invention provides a radiotherapy dose measurement and regulation device, which comprises a regulation box, a protection plate is arranged on the surface wall of the regulation box, and the protection plate and the regulation box are of a detachable structure; wherein a turnover limiting plate is arranged on the surface wall of the regulation and control box, a connecting plate is arranged at the top end of the regulation and control box and located on one side of the limiting plate, the regulation and control box needs to be controlled, the turnover limiting plate and the connecting plate make contact with each other and are adsorbed, and the regulation and control box needs to be controlled. According to the invention, the internal operation keys and the touch screen can be operated, and the overturned limiting plate and the connecting plate at the top end of the regulation and control box are mutually contacted and adsorbed in a magnetic adsorption manner, so that the overturned limiting plate is fixed, dust is effectively prevented from entering and mistaken touch operation is effectively prevented, and the operation efficiency is improved. Normal operation and regulation and control accuracy of the device are guaranteed, the dustproof capacity of the device is greatly improved, dust is effectively prevented from entering the device, and accuracy and stability of measurement and regulation and control are guaranteed.

The invention relates to the technical field of radiotherapy dose verification, and particularly discloses a cyber knife radiotherapy plan point dose and plane dose synchronous verification method, which comprises the following steps: constructing cyber knife patient dose verification equipment by a pinpoint ionization chamber, an EBT3 film, solid water and a gold label; scanning the verification device by using the simulated positioning CT to obtain a verification device CT image; the CT image of the verification equipment is guided into the treatment plan of the wave knife patient, and a model body plan of gold mark tracking is generated; registering the CT image of the patient radiotherapy plan with the CT image corresponding to the motif plan, and transplanting beam information in the patient radiotherapy plan to the motif plan; a high-resolution beam tracking algorithm is adopted to recalculate the dose distribution of the treatment beam on the phantom plan image; and recording the median dose of the sensitive volume of the ionization chamber, and exporting the dose distribution diagram of the layer where the film plane is located in the motif plan so as to carry out absolute point dose analysis and plane dose analysis. According to the invention, the error detection rate of patient plan verification can be improved.

The invention discloses a forward LatticeRT dose distribution optimization method and device based on an orthogonal double-layer grating. The method comprises the following steps: setting a prescription dose, arranging a radiation field with an equal interval angle, setting a blade position of an upper-layer grating, and forming a vertical stripe-shaped irradiation area; dose calculation is carried out in the forward direction, and a prescription dose line is stored as an organ structure; setting an irradiation range, and adjusting an irradiation angle within the irradiation range; according to the position of the structure on the BEV, the blade position of the upper grating is adjusted, so that the irradiation range can cover the structure; setting the blade position of the lower grating to form a lattice radiotherapy dose mode; generating a lattice wave crest irradiation area and a wave trough irradiation area according to the dose distribution; setting a comprehensive objective function; and an iterative optimization algorithm is adopted to adjust the blade position and the radiation field weight, so that the dose distribution is gradually close to the target function. The unique space structure of the orthogonal double-layer grating is utilized, the wave crests and the wave troughs are rapidly formed, and the Lattice plan manufacturing process is simplified.