130 results about "Dose prediction" patented technology

The invention discloses an intelligent nuclear radiation dose real-time monitoring system based on deep learning, and the system comprises the steps: S1, a multi-mode sensing module which is used for outputting radiation intensity data with timestamps, environment image data and geographic space position information; s2, a preprocessing module used for outputting a structured feature sequence; s3, a fusion modeling module which is used for generating a unified fusion feature vector; s4, an end-edge reasoning module which is used for processing the fusion feature vector in stages and generating a nuclear radiation dose prediction result; s5, the task scheduling module is used for dynamically determining whether the reasoning task is executed on the end side or the edge node; s6, an anomaly detection module which is used for determining whether there is an abnormal change of the nuclear radiation dose; and S7, the system control module is used for coordinating the modules. The method has the advantages of being high in environmental adaptability, fast in reasoning response and high in anomaly recognition precision.

The invention provides a pelvic tumor multi-center, multi-tumor-species and multi-prescription dose prediction method and related equipment, and is applied to the technical field of data processing. The method comprises the following steps: preprocessing a training sample set to generate a training sample set with target feature data; processing a preset dose distribution processing model based on a SwinTransform network architecture based on the training sample set with the target feature data, and generating a target dose distribution processing model; performing feature extraction processing on the CT image information of the target user to generate a target feature vector; processing the physiological state information of the target user to generate a dose limiting factor of the target user; processing the physiological parameter information of the target user to generate a dynamic dose influence factor of the target user; and processing the target feature vector, the dose limiting factor of the target user and the dynamic dose influence factor of the target user based on a target dose distribution processing model to generate target dose distribution information.

The invention belongs to the technical field of radiation protection, and particularly relates to a neutron shielding material optimization method based on machine learning and chaotic mapping NSGA-III, and the method comprises the steps: 1, determining an optimization target, and building a multi-target neutron composite shielding material attribute optimization model; step 2, building a Monte Carlo model to simulate and calculate shielding doses of the composite shielding materials with different attributes to a neutron source, and obtaining a data set with the material component proportion and thickness as characteristic attributes and the shielding doses as labels; and step 3, optimizing initial parameters of the BP neural network by adopting an improved particle swarm optimization algorithm, improving calculation precision and operation efficiency of the neural network, and training a neural network regression model by taking the data set obtained in the step 2 as a sample to complete establishment of a shielding dose prediction model and the like. An improved particle swarm optimization algorithm for improving Levy flight mechanism strategy optimization is designed to optimize initial parameters of the BP neural network, and the precision and operation efficiency of a neural network shielding dose regression prediction model are improved.

The invention relates to the technical field of medical drug dose analysis, in particular to a deep learning-based advanced nasopharyngeal carcinoma multi-target dose prediction system, which comprises a data acquisition module, an automatic sketching module, a dose prediction model construction module, a diversified dose generation module, a model training and optimization module, a result output and visualization module and the like. The automatic sketching module is used for realizing accurate and automatic sketching by using a multi-modal fusion and knowledge distillation framework; the dose prediction module accurately extracts features based on a UNetr network model and generates a multi-channel dose prediction result; the diversified dose generation module generates diversified complementary dose distribution through a specific method and a constraint mechanism; the model training and optimizing module effectively improves the model performance; the result output and visualization module presents results in various visual modes for doctors to make decisions; the nasopharynx cancer treatment dose prediction accuracy and individuation degree can be improved, the automatic sketching precision and efficiency can be improved, the clinical decision process can be optimized, and development of related technologies can be promoted.

The invention provides a nasopharynx cancer radiotherapy plan dose prediction method based on a progressive feature fusion network, and the method comprises the steps: obtaining image data which comprises a CT image, a target region, the annotation information of an organ at risk, and the dose distribution of a radiotherapy plan, and processing the image data into an input image of a preset size; inputting the input image into a dose prediction model to train the dose prediction model, and determining a target dose prediction model; a to-be-recognized medical image is input into a target dose prediction model, a nasopharyngeal carcinoma radiotherapy plan dose prediction result is output, and specifically, a strategy of progressive feature fusion and wavelet convolution-based pixel attention guidance fusion is introduced into the dose prediction model, so that discriminative semantic features can be effectively extracted, the semantic distinguishing capability of a network model is enhanced, and the prediction accuracy of the nasopharyngeal carcinoma radiotherapy plan dose prediction result is improved. And meanwhile, deep-level semantic discrimination information and shallow-level spatial positioning details are effectively fused, and the multi-level information fusion capability of the network model is enhanced.

A machine learning-based method of generating a radiotherapy treatment plan for a patient, comprises dose prediction and dose mimicking, wherein the dose prediction step involves using a machine learning system that has been trained to consider at least one optimality criterion related to physical or technical restrictions that will affect the delivery of the treatment plan. Thus, at least one of the factors that are normally taken into account in the dose mimicking step is introduced in the dose prediction step. The invention also relates to a method of training such a machine learning system for use in radiotherapy treatment planning, a computer program product and a computer system.

The invention relates to the technical field of radiotherapy dose prediction, in particular to a glioma radiation dose prediction system based on an attention-enhanced convolutional neural network, which comprises a data collection module, a data preprocessing module, a convolutional neural network module, an attention mechanism module and an output layer. According to the invention, by introducing the attention mechanism, the important feature region can be effectively highlighted, so that the prediction accuracy of the radiation dose of the glioma patient is improved, and the individuation and precision of the treatment scheme are ensured; a dose distribution result can be quickly generated through automatic dose prediction, time and energy investment of doctors in radiotherapy plan design is reduced, working efficiency is improved, accuracy and efficiency of glioma radiotherapy are remarkably improved, clinical decisions are optimized, medical cost is reduced, and support is provided for intelligentization and automation of radiotherapy. Through the research of the project, the application of the deep learning technology in the field of medical image analysis is promoted, and the overall level of radiotherapy is improved.

The invention provides a cone beam CT-based pelvic tumor adaptive radiotherapy dose prediction method and system, and is applied to the technical field of data processing. The method comprises the following steps: preprocessing a cone beam CT image of a target user to generate a target cone beam CT image; processing the target cone beam CT image and a preset CT image of the target user to generate preset target region sketching information; processing the physiological motion parameter information of the target user and the target physiological structure information of the target user to generate a dynamic dose influence factor; processing the initial image recognition processing model based on the training sample set to generate a target image recognition processing model; processing the target cone beam CT image and the preset target region sketching information based on a target image recognition processing model to generate a target CT image; and processing the target CT image, the dynamic dose influence factor and the physiological state information of the target user based on the target image recognition processing model to generate target dose prediction information.

A method and system for intelligent plan optimization. The method may include: obtaining a sample scan image, one or more sample contour images, and sample dose data by resampling a scan image, a contour image, and dose data at a same resolution; obtaining a weighted image by the one or more sample contour images; obtaining a sample weighted image by performing a dimensionality reduction on the weighted image; obtaining a trained regression model by performing a regression training on an initial regression model based on the sample scan image, the contour image of the sample target region, the sample weighted image, and the sample dose data corresponding to the sample scan image; and obtaining a dose prediction result by inputting a to-be-tested scan image, a to-be-tested contour image of the target region, and a to-be-tested weighted image after the dimensionality reduction into the trained regression model.

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 multi-modal imaging system and method for a small animal living body. The system comprises a multi-modal fusion imaging module which is responsible for multi-modal image acquisition and primary processing; the real-time dynamic monitoring module is used for capturing bioluminescence / fluorescence signals in real time through a photon counting detector, receiving optical, nuclear medicine and magnetic resonance data, correcting motion artifacts based on a multi-scale space-time registration engine and adjusting scanning parameters through a real-time pharmacokinetic-physiological feedback mechanism; the low-radiation and biological compatible module is used for automatically optimizing the dosage of a tracer agent according to the weight of the animal, the scanning part and historical data by using a dosage prediction model; and the multi-modal data fusion module is used for performing non-rigid registration of multi-modal images based on a Transform cross-modal registration network, constructing a multi-species pharmacokinetic knowledge graph by utilizing multi-species metabolism chip data, integrating mouse, dog, primate and humanized liver chip data, and mining a cross-species metabolism rule through a graph neural network.

The invention proposes a voxel-level dose distribution prediction method, and the method comprises the steps: obtaining a detection image of a target object when each tracer is independently introduced under the condition that a plurality of tracers are independently introduced into the target object; performing image standardization processing on the detection image corresponding to each tracer agent to obtain a standardized image corresponding to each tracer agent; and taking the standardized images corresponding to the various tracers as input information of a deep learning model, and determining a comprehensive three-dimensional dose prediction result of the target object under the various tracers through the deep learning model. According to the technical scheme, the accuracy of dose prediction in nuclide therapy is improved, full-link automation from image acquisition to dose planning is realized, the generated dose distribution not only meets the clinical requirement of precise coverage of a target region, but also conforms to the radiotherapy principle of normal tissue protection, and reliable technical support is provided for personalized precise radiotherapy.

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 radiotherapy intelligent planning method and system, and the method comprises the steps: inputting the radiotherapy plan input information of a patient, and predicting the three-dimensional dose distribution of the patient through a dose prediction model; generating an initial radiotherapy plan based on radiotherapy plan input information of the patient and the three-dimensional dose distribution; and performing automatic learning parameter adjustment and optimization on the initial radiotherapy plan through the deep Q network model to obtain a final radiotherapy plan meeting clinical requirements. According to the radiotherapy intelligent planning method, automatic prediction of three-dimensional dose distribution is realized through the dose prediction model, automatic parameter optimization is carried out on the initial radiotherapy plan of the three-dimensional dose distribution through the depth Q network model, and finally, the final radiotherapy plan meeting clinical requirements is automatically generated, so that the radiotherapy efficiency is improved. According to the radiotherapy intelligent planning method, a model combining dose prediction and automatic parameter optimization is established, automatic, accurate and efficient radiotherapy plan optimization is realized, and the method has important practical value.

The invention relates to the technical field of industrial spray dust fall and the technical field of computers, and discloses a spray dust fall method and system based on a multi-field multi-phase model and a deep learning algorithm. The method comprises the following steps: acquiring dust data and environment data of a region to be subjected to dust fall; according to the dust particle size data, the dust density data, the wind speed data and the air density data, the dust migration speed under the multi-field and multi-phase theory is calculated; carrying out characterization processing on the dust data and the dust migration speed; according to the processing result and the trained spray solution parameter prediction model, generating a spray solution type and dosage prediction result; and according to the prediction result, dust falling of the area to be subjected to dust falling is realized. According to the method, the dust migration speed calculated based on the multi-field and multi-phase model and the spray solution parameter prediction model based on the deep learning algorithm are utilized to calculate, predict and update the dust data of the area to be subjected to dust falling, so that more accurate spray solution types and doses can be obtained, and the dust falling effect of the area to be subjected to dust falling is remarkably improved.

The invention discloses a patient historical state-based anesthetic dose simulation evaluation system, which is characterized by comprising a multi-source data integration and preoperative physiological feature quantification layer, a dose effect relationship simulation layer and a risk evaluation and recommendation output layer. The method has the following advantages and effects that the personalized pharmacokinetics-pharmacodynamics model is constructed by integrating multi-source historical data of a patient, vital sign change tracks under different anesthetic doses are simulated, the recommended dose interval is generated based on multi-objective optimization, the change from experience dependence to accurate quantification is realized, and the accuracy of the recommended dose interval is improved. The problem of inaccurate anesthetic dosage prediction caused by individual differences is effectively solved, and the safety and effectiveness of an anesthesia scheme are remarkably improved.

The invention discloses a plan evaluation method and system based on intelligent dose prediction, and the method comprises the steps: carrying out the dose prediction according to the information of a patient, generating a radiotherapy plan, and obtaining the three-dimensional dose distribution; calculating to obtain an optimized organ point predicted dose according to the dose distribution voxel point dose in the predicted three-dimensional dose distribution; constructing an objective function combined by the organ point predicted dose and the constraint term, and performing optimization to obtain a radiotherapy optimization plan; and evaluating the optimization quality of the radiotherapy plan by comparing the DVH images of the radiotherapy plan and the radiotherapy optimization plan, and determining whether the optimization of the radiotherapy plan is completed or not according to the evaluation result. According to the method, plan optimization and constraint weight correction are carried out by using the target function combining the organ point predicted dose and the constraint term, a larger solution space is provided, and meanwhile, the predicted dose is taken as a standard for measuring plan quality by an index, so that a physician can intuitively know the plan optimization degree, and the plan optimization efficiency is improved. And a high-quality plan is obtained as much as possible.

The invention discloses a machine learning-driven water body bacteria ultraviolet disinfection dosage prediction and optimization method, which comprises the following steps of: obtaining genetic information, environmental parameters and ultraviolet disinfection conditions of target bacteria, and numeralizing the genetic information into feature vectors; cleaning and coding the acquired data to form a feature matrix; based on the characteristic matrix, a machine learning algorithm is utilized to train an ultraviolet disinfection dosage prediction model, and the model takes the inactivation efficiency as a prediction target; according to bacterial genetic information, environmental parameters and ultraviolet conditions input by a user, utilizing the trained prediction model to predict the inactivation efficiency, scanning in a multi-dimensional parameter space, and determining an optimal ultraviolet disinfection condition combination meeting a target inactivation level; outputting an optimal ultraviolet disinfection condition combination; the method disclosed by the invention has cross-strain and cross-scene applicability.

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 graded blasting construction method for a shallow-buried soft rock tunnel sensitive section. The method comprises the steps that measuring points are arranged in a tunnel chamber, a ground surface non-cavity section and a cavity section, and blasting vibration data are obtained; peak vibration velocity prediction models are established in a partitioned mode, an attenuation model is adopted in a chamber, a correction term is introduced into a non-cavity section of the earth surface to correct the attenuation model, and a multivariable model is established for the cavity section through dimensional analysis by integrating the horizontal distance, the elevation difference, the explosive quantity, the rock mass density and the tensile strength; determining a ground surface vibration velocity safety threshold, and quantifying a cavity vibration velocity amplification coefficient and an influence range thereof; based on the peak vibration velocity probability distribution and a preset safety level, establishing a safe dose prediction model and a safe range model; and dividing control areas according to the model, and implementing graded construction. According to the method, the vibration propagation law can be accurately described, the cavity amplification effect can be quantified, dynamic prediction of the blasting safety explosive quantity and range is achieved, and disturbance of blasting vibration to surrounding rock is effectively controlled through hierarchical management and control.

The invention discloses a radiotherapy robustness optimization method and system, and the method comprises the steps: obtaining the clinical diagnosis and treatment information and anatomical feature information of a patient, and outputting unbiased scene dose three-dimensional distribution based on a dose prediction model; setting the maximum placement deviation of the patient, and obtaining deviation scene dose three-dimensional distribution corresponding to each placement deviation scene based on the dose three-dimensional distribution of the patient randomly moved in the maximum placement deviation range; for each round of circulation of robustness optimization, performing optimization calculation on the dose three-dimensional distribution of the unbiased scene to obtain optimized dose distribution, and calculating the sum of difference values between the optimized dose distribution and the dose three-dimensional distribution of each biased scene to obtain a target function value; when robustness optimization iteration reaches a preset number of times, optimization is ended, and a final optimization plan and corresponding final dose three-dimensional distribution are obtained; and for the final optimization plan, moving the final dose three-dimensional distribution in the maximum positioning deviation range, obtaining a deviation scene DVH curve, and evaluating the final optimization plan.

The invention provides a dose prediction method, which comprises the following steps: generating an initial vector based on initial prescription information and patient structure data; generating an initialization parameter based on the initial vector; generating initial dose distribution according to the initialization parameters; and adjusting the initial dose distribution based on a plurality of agents, and generating a target dose distribution, where a single agent is responsible for at least partial adjustment of the initial dose distribution. The invention further provides a device for executing the method and electronic equipment.

The present invention provides a method for predicting dose distribution of boron neutron capture therapy based on artificial intelligence, and its application and device. The method includes: obtaining CT images, characteristic prompt gamma ray yield distribution samples and dose distribution samples of patients; using characteristic prompt gamma ray yield distribution samples, dose distribution samples and CT images to train and test artificial intelligence network models; in the process of boron neutron capture therapy, using radiation detection devices to detect characteristic prompt gamma rays, and reconstructing characteristic prompt gamma ray yield distribution in the target area; and predicting dose distribution based on the trained artificial intelligence network model, the reconstructed characteristic prompt gamma ray yield distribution and CT images. The present invention has the characteristics of fast, accurate, simple and easy to implement, and can effectively solve the problem of inaccurate dose prediction during BNCT treatment, and effectively ensure the treatment effect of BNCT.

A dose prediction method and system, the method comprising: acquiring an original dose distribution of a radiotherapy object, original sign information and current sign information; obtaining a predicted Pareto optimization parameter through a dose prediction model according to the original dose distribution and the original sign information; and predicting a current dose distribution corresponding to the current sign information by using the dose prediction model according to the current sign information and the predicted Pareto optimization parameter.

The invention provides a flexible solar cell irradiation attenuation coefficient estimation method comprising the following steps: orbital parameters are combed, and spacecraft orbital parameters and space environment parameters are obtained; based on the orbit parameters and the space environment parameters, establishing an irradiation dose calculation model of the flexible solar cell; performing a ground equivalent test based on the irradiation dose calculated by the irradiation dose calculation model; and determining the irradiation attenuation coefficient of the solar cell in combination with test data and historical data. According to the method for estimating the irradiation attenuation coefficient of the flexible solar cell, the irradiation dose calculation model suitable for the flexible solar cell is constructed, the on-orbit performance attenuation coefficient of the solar cell array can be accurately obtained, and the accuracy of irradiation dose estimation is improved; scientific basis is provided for solar cell array design and on-orbit energy management, and reliability and design rationality of a spacecraft energy system are effectively improved.

A method (100) for generating a treatment plan specifying an irradiation of a patient, the method comprising: a dose inference stage (112), including using a model to infer a spatial dose from patient data; a dose mimicking stage (116), including executing a robust optimization process to generate a deliverable treatment plan which is consistent with the inferred spatial dose, wherein the robust optimization considers a plurality of scenarios relating to patient data uncertainty.

The invention discloses a proton treatment plan verification and optimization device and method and related equipment, and relates to the technical field of radiotherapy. The device comprises an image acquisition module which is used for acquiring a PET image of a patient after completing at least one proton treatment irradiation; the dose prediction module is used for mapping the treated PET image into actual deposited dose distribution in the body of the patient based on the trained generative model so as to predict actual distribution dose distribution; and the adjustment and optimization module is used for generating an adjusted fractional treatment plan when the deviation between the predicted actual distribution dose distribution and the initial planned dose distribution is greater than a deviation threshold value. Based on in-vivo measurement information, the actual deposition dose is accurately verified and dynamically optimized, and accurate and safe proton therapy is achieved.

The application provides a kind of based on deep learning's absorbed dose prediction method, device, equipment and medium, it is related to medical image processing technical field, comprising: obtaining the treatment of patient's pre-test multi-modal data;According to clinical examination data and the mask map of the target tumor region and normal key organ region of pre-set drug, space coding is carried out, and the biomarker feature map of the patient to be tested is constructed;According to the pre-treatment medical image and biomarker feature map, a preset first deep learning model is used for prediction, to generate the simulated treatment initial medical image of the patient to be tested;According to the simulated treatment initial medical image, a preset second deep learning model is used for prediction, to generate the post-treatment predicted dosimetry parameter map of the patient to be tested.The application can improve the accuracy of nuclide absorbed dose prediction evaluation.