44 results about "Adaptive radiotherapy" patented technology

A computer-implemented system for improving radiation dose reconstruction in adaptive real-time radiotherapy, comprising a data acquisition module for capturing treatment data in real time during radiotherapy, a preprocessing module for preprocessing the captured data, a deep-learning dose reconstruction engine for reconstructing the delivered radiation dose distribution using a trained deep-learning model, a dose evaluation and comparison module for comparing the reconstructed dose distribution with a planned dose distribution, and an adaptive decision support module to assist in adjusting radiotherapy treatment parameters based on the comparison results.

The invention provides a cervical cancer self-adaptive radiotherapy assessment decision method and device, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: obtaining a cone beam CT image and a plan CT image before radiotherapy of a patient to be subjected to radiotherapy on the day of radiotherapy, and extracting the structural quantitative features of the images; the method comprises the following steps: calculating a difference value of structured quantitative characteristics between a cone beam CT image on the day of radiotherapy and a plan CT image before radiotherapy to obtain multi-dimensional geometric change characteristics; and inputting the multi-dimensional geometric change features, the vectorized representation of the predetermined key region of the cone beam CT image and the clinical text information into a pre-trained clinical large language model, and outputting a self-adaptive radiotherapy demand tag. The problem that the prior art depends on manual analysis, and it is difficult to compare the change characteristics of the CT image between the radiotherapy day and the plan before radiotherapy to automatically analyze whether self-adaptive radiotherapy is executed on the radiotherapy day is solved.

The invention relates to the technical field of image guidance positioning, and provides a radiotherapy positioning system and a positioning structure based on image guidance, which are characterized in that a comprehensive, accurate and adaptive radiotherapy positioning system is constructed, the positioning structure is monitored and analyzed in real time from multi-modal image data fusion in combination with physiological features of target tissues, and the positioning structure is used for positioning the target tissues. In the radiotherapy process, parameters of the treatment bed and the positioning structure can be dynamically and accurately adjusted according to the actual position and state change of the target tissue, the integration mode effectively avoids the problem of positioning deviation caused by the fact that a traditional positioning method is single and lacks real-time dynamic adjustment, and the positioning accuracy is improved. The overall precision and reliability of radiotherapy positioning are greatly improved, and a solid guarantee is provided for accurate action of radiotherapy on target tissues.

Techniques for adjusting radiotherapy treatment for a patient in real-time are provided. The techniques include obtaining a training patient anatomy at a first time within a training radiotherapy treatment fraction after a training radiotherapy treatment dose has been delivered by a radiotherapy device; computing a deviation between the training patient anatomy at the first time and reference training patient anatomy during the training radiotherapy treatment fraction, wherein the reference training patient anatomy indicates a prescribed training dose parameter to be delivered within the training radiotherapy treatment fraction; applying the computed deviation to a machine learning model to estimate one or more intra-fraction radiotherapy treatment parameters of a function that provides a radiotherapy device parameter adjustment based on the one or more intra-fraction radiotherapy treatment parameters; and training the machine learning model to establish a relationship between the computed deviation and the one or more intra-fraction radiotherapy treatment parameters.

Systems, devices and methods for using similarity metrics to determine optimization strategy in adaptive radiation therapy, and systems and methods for an automated adaptive workflow to automatically adapt and optimize a treatment plan to a current treatment session using MLC leaf configurations selected based on calculated similarity metric values.

The invention relates to the technical field of radiotherapy, and discloses a multi-modal image fusion guided chest tumor adaptive radiotherapy system. The system comprises an asynchronous bimodal sensing module, a spectrum preprocessing module, a parameter inversion module, a boundary construction module and a biomechanical calculation module. According to the method, the feedback voxel-level velocity field is utilized to execute Lagrange reverse warping on the high-energy projection with physical time delay, and motion artifacts are eliminated; and dynamically updating the Young modulus of the lung tissue mechanical model based on the inverted real-time electron density, and solving a full-field three-dimensional displacement field in combination with double boundary conditions of body surface flexibility and internal skeleton rigidity so as to reconstruct a tumor target region and generate a beam control instruction. According to the method, through closed-loop interaction of image physics and biomechanics, the problems of dual-energy imaging motion artifact interference and insufficient lung tissue fixed parameter modeling precision are solved, and accurate self-adaptive tracking of a tumor target region is realized.

A radiotherapy system comprising a radiation delivery system 100 and a computer data store configured to store a radiotherapy treatment plan. The radiotherapy treatment plan comprises a plurality of c

A rapid verification method for adaptive radiotherapy dose consistency in the TOMO system is disclosed, relating to the field of medical information technology, particularly TOMO radiotherapy information processing. The method includes: acquiring flux distribution data and the original region of interest (ROI) from the original radiotherapy plan; projecting the ROI onto the flux projection space based on the beam geometry parameters of the original radiotherapy plan, and establishing a mapping relationship as a reference index; acquiring the target ROI for the adaptive radiotherapy phase; updating the mapping relationship based on the flux distribution data and the target ROI, and using the mapping relationship as the target index; detecting the data consistency between the target index and the reference index, and determining the dose distribution change information of the target object. This application solves the technical problems in existing technologies where adaptive radiotherapy dose consistency verification relies on a high-load dose calculation engine for data reprocessing, resulting in large amounts of computational data, high system response latency, and inability to meet the timeliness requirements for online verification.

The invention discloses a proton adaptive radiotherapy triggering decision method and system based on multi-modal data quantitative evaluation. The method comprises the following steps: acquiring image data, dose data and clinical data, respectively calculating a dosimetry scoring function, an anatomical structure scoring function and a proton specificity scoring function based on the image data and the dose data, obtaining an enhanced core quantitative score through weighted geometric average fusion, and obtaining the enhanced core quantitative score; determining parts, stages and prognosis adjustment parameters based on clinical data, and fusing a BMI dynamic evaluation factor, a treatment response prediction factor and a multi-factor interaction factor to obtain an enhanced clinical background adjustment coefficient; and calculating a final decision score in combination with the time modulation factor and the risk prediction index based on the LSTM, and outputting a grading decision result. According to the method, objective quantitative evaluation of deep fusion of multi-modal data is realized, specific risks of proton therapy are accurately captured, personalized intelligent decision is supported, and the method has good clinical operability.

This application discloses a real-time adaptive radiotherapy management system, relating to the field of medical technology. The system includes: a real-time data processing unit for acquiring real-time data of the target object during radiotherapy; an adaptive decision-making unit for determining a target radiotherapy plan matching the current state of the target object from multiple alternative radiotherapy plans based on the real-time data, wherein the multiple alternative radiotherapy plans are associated with different physiological motion phases or anatomical states of the target object; a plan execution sequence splitting unit for parsing and splitting each alternative radiotherapy plan into one or more hardware execution sequences; and an execution scheduling unit for sending at least one hardware execution sequence corresponding to the target radiotherapy plan to the control system of the radiotherapy equipment. This application solves the technical problem that existing adaptive radiotherapy systems, due to their rigid architecture and singular decision-making mode, cannot achieve highly real-time dynamic plan switching during treatment.

The invention relates to a delivery dose control method for adaptive radiotherapy, computer equipment, a storage medium and a program product. The method comprises the following steps: collecting a positron signal while carrying out first delivery on beam energy of an initial dose to a target position; stopping beam irradiation in a preset time period, and collecting a positron signal in the preset time period to generate a positron activity distribution image; registering the positron activity distribution image with a preset template, and calculating to obtain a deviation value; judging whether the deviation value exceeds a preset threshold value or not; when the judgment result is yes, the irradiation parameters of the beam are adjusted, and the target position is covered with the beam energy of the residual dose; wherein the residual dose is greater than the initial dose. Through staged dose delivery, a small amount of dose is delivered first, then the acquired activity distribution image is registered with the preset template, and the remaining dose delivery is adjusted in real time according to the deviation value, so that the subsequent dose can cover the target position, and the method is suitable for the radiotherapy plan in different times, and the dose delivery precision is high.

The invention relates to a multi-modal medical image deep learning enhanced segmentation system and a method thereof, belongs to the field of medical image processing, and aims to solve the problem of inaccurate brain glioma target region boundary segmentation. According to the technical scheme, the method comprises the steps that a metabolic domain PET image and a structural domain MRI-T2 image are obtained, and image registration, noise suppression and intensity standardization preprocessing are carried out; glycolysis features and texture anisotropy features are extracted through a double-domain feature decoupler; constructing a graph neural network to generate a metabolism-structure interaction graph; calculating a pathological infiltration confidence coefficient thermodynamic diagram based on the atlas; the thermodynamic diagram is used for self-adaptive radiotherapy dose segmentation, and the dose gradient is dynamically adjusted; and finally outputting the tumor target boundary.

An adaptive radiotherapy system (100) with biophysical real-time monitoring of the tissue response, wherein the system (100) comprises: a module (1) for real-time biophysical tissue response monitoring, configured to acquire live data on the physiological response and tissue response from a treatment area of ​​a patient during irradiation; a module (2) for localizing patient anatomy and motion detection, configured to detect position changes, organ movements and target shifts in real time; an engine (3) for adaptive treatment planning and dose recalculation, configured to dynamically update a radiotherapy treatment plan based on data received from the module (1) for real-time biophysical monitoring of tissue response and from the module (2) for localizing patient anatomy and motion detection; an engine for predictive modeling of tissue response (4) configured to estimate short-term tissue response, tumor response and normal tissue tolerance using patient data from current and previous sessions; an interface for controlling and modulating the beam (5) which is functionally coupled to a radiation delivery device and configured to modify one or more beam parameters in real time; a safety check and treatment lock control (6) configured to compare current treatment conditions with one or more allowable thresholds and selectively interrupt, stop, or release radiation delivery; and a module (7) for clinical review, data logging and treatment evaluation, configured to store treatment response data, generate session-related adjustment protocols and provide one or more recommendations for subsequent fractions, wherein the system (100) adaptively modifies the radiation output in response to continuously monitored biophysical tissue conditions of the patient.

Provided in the present disclosure are an adaptive radiotherapy plan optimization method and an electronic device. The method comprises: acquiring a radiotherapy plan consisting of I fractions of radiotherapy formulated for a target volume of a subject to be subjected to radiotherapy, I being an integer greater than 1; before an i-th fraction of radiotherapy starts, acquiring a morphological and positional image of said subject, and on the basis of said image, optimizing the radiotherapy plan for said subject, said image being used for indicating the morphology and / or position of the target volume of said subject, the i-th fraction of radiotherapy being the i-th fraction of radiotherapy among the I fractions of radiotherapy, and i being 1, 2, 3, ... I; and during the i-th fraction of radiotherapy, within a continuous radiation beam-on duration and / or within a radiation beam-off duration before the continuous radiation beam-on duration, acquiring a morphological and positional image of said subject, and on the basis of the morphological and positional image, optimizing the optimized radiotherapy plan for said subject.

A computer-implemented method for adaptive radiotherapy can comprise receiving symptom data, the symptom data being indicative of at least one of an existence or a severity of at least one of a symptom or a side effect experienced by a radiotherapy patient undergoing treatment according to a first radiotherapy treatment plan. The method can further comprise obtaining first treatment plan data associated with the first radiotherapy treatment plan, and generating a second radiotherapy treatment plan based on the obtained first radiotherapy treatment plan data and the symptom data.

Disclosed is a determination method for adaptive radiotherapy. The method includes: obtaining a current fraction image corresponding to a current radiotherapy fraction of a target subject, and obtaining an initial image configured for determining an initial radiotherapy plan for the target subject; determining a trigger analysis result according to the current fraction image, the initial image, and a trigger analysis model; and determining whether to perform adaptive radiotherapy based on the trigger analysis result.

A method of treating a tumor in a subject, the method comprises applying a tumor treating field to the tumor at a frequency between approximately 50 kHz and approximately 1,000 kHz; and delivering personalized ultra-fractionated adaptive radiotherapy (PULSAR) regimen.

The invention provides an artificial intelligence guided radiotherapy accurate positioning system, and relates to the technical field of medical treatment, the system comprises a data acquisition and processing module, an anatomical change intelligent analysis module and a positioning guide instruction generation module, and the anatomical change intelligent analysis module comprises a deformation registration unit and an intelligent judgment unit; according to the method, the difference between the current anatomical structure and the original plan is automatically compared before each treatment, and the clinical significance of the change is intelligently judged, so that a differentiated precise guide strategy is realized: when the change is not significant, the system quickly completes high-precision positioning verification, the treatment preparation time is greatly shortened, and the treatment efficiency is improved; when the change is obvious, the system not only can accurately map the target area and calculate the current positioning error, but also can actively send out early warning needing doctor intervention, thereby effectively avoiding the risk of target area leakage irradiation or excessive irradiation of normal tissues caused by the change of the anatomical structure, and providing key decision support for clinical implementation of adaptive radiotherapy.

The invention discloses a self-adaptive radiotherapy plan re-optimization method and system, an electronic device and a medium wherein the self-adaptive radiotherapy plan re-optimization system comprises a deformation registration module for performing deformation registration to obtain a deformation field; the multi-time plan data module is used for importing multi-time plan data which comprises an ROI (Region of Interest) sketching area; the multi-time plan creating module is used for creating multi-time plan parameters; the deformation identification module is used for carrying out deformation analysis on the deformation field mu and identifying a deformation area; the grid optimization module is used for adjusting the priority of the ROI sketching region and optimizing the dose grid resolution of the deformation region to obtain the optimized dose grid resolution and distribution thereof; the plan re-optimization module is used for performing re-optimization to obtain an optimized radiotherapy plan; according to the method and the device, the technical problem that the dose optimization speed is difficult to improve under the condition of relatively low dose error in the setting of the radiotherapy dose grid resolution in the prior art is solved.

The invention relates to the technical field of medical instruments, and provides a self-adaptive radiotherapy body position fixing device capable of generating deformation based on pressure feeling in order to solve the problems that an existing radiotherapy body position fixing mode is insufficient in adaptability and cannot be reused, the self-adaptive radiotherapy body position fixing device comprises a body frame plate, and a plurality of fixing grooves distributed in a human-shaped array are formed in the body frame plate in a penetrating mode; the pressure sensing mechanism is arranged in each fixing groove in a penetrating manner, and is configured to sense the pressure distribution and the pressure of a contact area of the body of the radiotherapy patient and the body frame plate in real time, and generate elastic deformation of a corresponding degree based on pressure change so as to adaptively fit the contour of the body of the patient; and the thermoplastic film is detachably arranged on the body frame plate. Personalized adaptation of the body contour of the patient can be achieved, meanwhile, repeated utilization can be achieved, disposable consumables do not need to be replaced, the medical cost is greatly reduced, medical waste pollution is reduced, operation is convenient and fast, body position fixing and repeated calling can be rapidly completed, and radiotherapy accuracy, patient comfort and radiotherapy turnover efficiency are improved to a certain degree.

The invention discloses a multi-modal-based personalized radiotherapy evaluation and prognosis prediction method and system, and the method comprises the following steps: S1, obtaining multi-modal data, including treatment plan data, clinical and image data of a patient, and treatment execution dynamic data; s2, performing fusion analysis on the multi-modal data, and generating a plan robustness index and a personalized risk index; s3, performing dynamic prognosis prediction by using a machine learning model based on the plan robustness index, the personalized risk index and the dynamic features extracted from the treatment execution dynamic data; and S4, according to the prognosis prediction result, judging whether self-adaptive radiotherapy early warning is triggered or not. According to the invention, the method achieves the crossing of the radiotherapy plan from static evaluation to dynamic prediction and from isolated analysis to comprehensive decision, and can provide personalized and closed-loop quality management and prognosis early warning before, after treatment.

The invention discloses a prostate cancer ORT intelligent trigger decision-making device based on LLM, and relates to the field of medical science and technology, and the method comprises the steps: an obtaining unit is used for obtaining a multi-modal time sequence characteristic matrix of a target object in a preset time window; the feature processing unit is used for performing channel independent time sequence feature extraction and sparse bottleneck aggregation on the multi-modal time sequence feature matrix to obtain a low-dimensional feature matrix; the text processing unit is used for generating a statistical enhancement prompt text according to a target index change trend in the multi-modal time sequence feature matrix, and converting the statistical enhancement prompt text into a text embedding sequence; and the decision-making unit is used for performing adaptive multi-modal fusion on the low-dimensional feature matrix and the text embedding sequence, and reasoning and outputting a trigger decision-making result and a corresponding explanation text. The problems of model parameter explosion and serious overfitting caused by extremely high feature dimension and extremely few training samples in a prostate cancer online adaptive radiotherapy trigger decision technology in the prior art are solved.

A method for radiotherapy treatment plan verification during a fraction of an online adaptive radiotherapy treatment The method comprises obtaining a pre-treatment cone beam computed tomography, CBCT, image of a patient. The method further comprises adapting a treatment plan, based on the CBCT image. The method further comprises delivering a first portion of the adapted treatment plan to the patient. The method further comprises obtaining dose data from the delivery of the first portion. The method further comprises determining a dosimetric value associated with quality assurance of the adapted treatment plan, based on the dose data. If the dosimetric value is within an acceptable range, the method further comprises delivering a second portion of the adapted treatment plan. Or, if the dosimetric value is outside the acceptable range, the method further comprises stopping the delivery of the adapted treatment plan.

This disclosure provides an adaptive radiotherapy planning optimization method and electronic device. The method includes: acquiring a radiotherapy plan of I fractions for a target area of ​​a subject to be radiotherapy, where I is an integer greater than 1; before the start of the i-th fraction, acquiring a shape and position image of the subject to be radiotherapy, and optimizing the radiotherapy plan of the subject to be radiotherapy based on the shape and position image; wherein the shape and position image is used to indicate the shape and / or position of the target area of ​​the subject to be radiotherapy, and the i-th fraction is the i-th fraction of the I fractions, where i is 1, 2, 3, ..., 1; during the i-th fraction, within the continuous beam exit segment and / or in the non-exit segment before the continuous beam exit segment, acquiring a shape and position image of the subject to be radiotherapy, and optimizing the optimized radiotherapy plan of the subject to be radiotherapy based on the shape and position image.

A computer-implemented method is disclosed for generating a radiation treatment plan for a patient for at least one future radiation treatment session. The method comprises obtaining one or more images of the patient. The method further comprises, generating, based on the one or more images, a representation of a plurality of potential anatomical states of the patient which may occur during the future radiation treatment session. The method further comprises, generating, based on the representation, at least one treatment plan. Such a method can reduce treatment margins and can improve the adaptive radiotherapy process by improving speed, safety and reduce the need for physician presence.

The invention discloses a real-time adaptive radiotherapy management system, and relates to the field of medical science and technology. The system comprises a real-time data processing unit used for acquiring real-time data of a target object in a radiotherapy process; the self-adaptive decision-making unit is used for determining a target radiotherapy plan matched with the current state of the target object from multiple alternative radiotherapy plans of the target object based on the real-time data, and the multiple alternative radiotherapy plans are associated with different physiological motion phases or anatomical states of the target object; the plan execution sequence splitting unit is used for analyzing and splitting each alternative radiotherapy plan into one or more hardware execution sequences; and the execution scheduling unit is used for issuing the at least one hardware execution sequence corresponding to the target radiotherapy plan to a control system of the radiotherapy equipment. The technical problem that an existing adaptive radiotherapy system cannot realize high-real-time dynamic plan switching in the treatment process due to framework stiffness and single decision mode is solved.

Systems and methods are disclosed for processing cone beam computed tomography (CBCT) image data, relating to radiotherapy plans and treatments. Example operations for a radiotherapy workflow include obtaining a plurality of newly captured CBCT projections, the projections providing imaging of an anatomical region corresponding to a radiotherapy treatment region; reconstructing a quantitative CBCT image from the plurality of newly captured CBCT projections; identifying an anatomical change in the human patient based on comparing the representation of the anatomical region in the quantified CBCT image with the representation of the anatomical region in the planning image; and modify a treatment plan for the radiotherapy workflow based on the anatomical change, wherein the treatment plan provides dose calculations for the anatomical region. Further operations for training and using regression models to correct CBCT predictions and perform additional imaging and radiotherapy data processing operations are disclosed.

Described is an approach for tracking 3D organ motion in real-time using magnetic resonance imaging (MRI). The approach may include offline learning, which may acquire signature and 3D imaging data over multiple respiratory cycles to create a database of high-resolution 3D motion states. The approach may further include online matching, which may acquire signature data only in real-time (latency less than 0.2 seconds). From a motion state and motion signature database, the 3D motion state whose signature best (or sufficiently) matches the newly-acquired signature data may be selected. Real-time 3D motion tracking may be accomplished by performing time-consuming acquisition and reconstruction work in an offline learning phase, leaving just signature acquisition and correlation analysis in an online matching step, minimizing or otherwise reducing latency. The approach may be used to adapt radiotherapy procedures based on tumor motion using a magnetic resonance linear accelerator (MR-Linac) system.