92 results about "Adaptive radiotherapy" patented technology

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.

The invention relates to the technical field of computers, and discloses an automatic segmentation and intelligent plan generation method and device suitable for self-adaptive radiotherapy of prostate cancer. Comprising the following steps: acquiring medical image data of a treated object, clinical dose data of a target area and an organ at risk in a focus area of the treated object, and clinical treatment data of a preset priority level; performing automatic segmentation based on the medical image data to obtain target segmentation data; performing dose distribution prediction according to the target segmentation data to obtain initial dose distribution data, and performing iterative optimization on the initial dose distribution data by using clinical treatment data to obtain target dose distribution data of the treated object; and performing iterative optimization of a radiotherapy plan based on the target dose distribution data, and generating a target radiotherapy plan of the treated object. Therefore, on the basis of realizing accurate, efficient and automatic segmentation of the prostate cancer target region and the endangered organ, multi-round iterative optimization of dose distribution and the radiotherapy plan is carried out, and finally a high-quality target radiotherapy plan is obtained.

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 relates to a self-adaptive radiotherapy plan determination method, a self-adaptive radiotherapy system and a storage medium. The method comprises the following steps: acquiring a current region-of-interest segmentation result and current image features corresponding to a current image of an object; according to the segmentation result of the current region of interest and the current image features, candidate radiotherapy plans are selected from a plan library of the object, and the plan library comprises historical images of the object and corresponding historical radiotherapy plans; calculating a calculated dose distribution corresponding to the current image based on parameters in the candidate radiotherapy plan; determining the applicability of the candidate radiotherapy plan for the current image according to the calculated dose distribution, the segmentation result of the current region of interest and the features of the current image; and the target radiotherapy plan for the current image is determined based on the applicability, so that the generation efficiency and effectiveness of the adaptive radiotherapy plan are improved.

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.

Disclosed herein are systems and methods for adapting and / or updating radiotherapy treatment plans based on biological and / or physiological data and / or anatomical data extracted or calculated from imaging data acquired in real-time (e.g., during a treatment session). Functional imaging data acquired at the time of radiation treatment is used to modify a treatment plan and / or dose delivery instructions to provide a prescribed dose distribution to patient target regions. Also disclosed herein are methods for evaluating treatment plans based on imaging data acquired in real-time.

The invention relates to a self-adaptive radiotherapy dose optimization method and device and a storage medium, which are used for optimizing dose distribution in radiotherapy. Through combination of deep reinforcement learning (DRL) and a heuristic algorithm (a particle swarm optimization algorithm), the method maximizes the dose of a tumor area, and protects surrounding normal tissues at the same time. Firstly, an anatomical structure and a tumor area of a patient are extracted through medical image data, and initial dose distribution is generated; then, the deep reinforcement learning agent adjusts dose distribution according to the dynamic anatomical change of the patient, a heuristic algorithm is further optimized, and a local optimal solution is avoided; real-time feedback in the treatment process is used for adjusting dose distribution, and accuracy and timeliness of a treatment scheme are ensured. Finally, the generated optimal dose distribution scheme is provided for a clinician to refer, dose maximization of the tumor area is ensured, and normal tissue is protected. According to the method, the precision and effect of radiotherapy are improved, and a personalized treatment scheme is provided.

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.

Systems and methods for the automatic generation and optimization of radiation therapy treatment plans, and systems and methods for the automatic generation and optimization of an adapted plan in an adaptive radiation therapy workflow.

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.

The invention discloses a tumor radiotherapy patient symptom management and prognosis evaluation method and system, and relates to the field of tumor radiotherapy, and the method comprises the steps: obtaining the age data, biological data, clinical data and symptom data of a tumor radiotherapy patient; calculating a comprehensive symptom score through the dynamic symptom evaluation model and the symptom data; when the symptom comprehensive score exceeds a preset threshold value, automatically matching a targeting scheme according to the symptom dominant factor; performing correction calculation on the biological data and the clinical data based on the age data to obtain a biological prognosis index and a clinical prognosis index; performing difference significance judgment on the biological prognosis index and the clinical prognosis index, and calculating a comprehensive prognosis index; dynamically adjusting the radiotherapy plan according to the biological prognosis index, the clinical prognosis index and the comprehensive prognosis index; the weight coefficient is updated after the radiotherapy plan is adjusted each time; according to the invention, dynamic symptom evaluation, multi-dimensional prognosis evaluation and adaptive radiotherapy plan adjustment can be realized.

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 present embodiments relate generally to enabling an end user - e.g. medical physicists - to monitor radiation therapy and, with integration to the LINAC, adaptive radiotherapy. Embodiments record signals already released during radiotherapy, requiring no new irradiation of the patient for in vivo, in line, dose monitoring. Furthermore, as the transducers will be coupled to the patient with ultrasound gel, the present embodiments are not invasive to patients and will not introduce new discomfort during therapeutic procedures. The addition of the RiAD system provides the end user with additional information during the treatment of the patient, allowing for real-time beam tracking and dosimetry, enabling the end user to practice adaptive radiotherapy. The RiAD system enables the end user to ensure that proper dose delivery is occurring with each new beamlet and dose deposition, empowering them to adapt the medical treatment as it is performed and increase its precision in conventional electron beam radiotherapy, electron FLASH radiotherapy, and intraoperative electron beam radiotherapy.

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 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.

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.

A radiotherapy treatment apparatus for imaging and treating a patient, comprising: a radiotherapy system comprising a treatment radiation source configured to perform radiotherapy treatment of a patient by directing treatment radiation towards the patient, wherein the patient is located substantially within a treatment region defining a longitudinal axis, wherein the treatment radiation source is moveable with respect to the longitudinal axis to thereby enable a selection of a treatment direction in preparation for radiotherapy treatment of the patient; an imaging system comprising an imaging radiation source and a radiation detector, wherein the imaging radiation source is moveable with respect to the longitudinal axis and is arranged to direct imaging radiation towards the patient in an imaging direction, and wherein the radiation detector is arranged to measure the imaging radiation after interaction of the imaging radiation with the patient, wherein the radiation detector comprises one or more photon counting detector arrays defining a sensitive area of the radiation detector, and wherein the imaging system is configured to generate multi-spectral images of the patient based on measurements of the imaging radiation by the one or more photon counting detector arrays.

The invention discloses an automatic sketching method and device for a radiotherapy structure based on 2D data, and belongs to the field of medical image processing, and the method comprises the steps: S1, collecting a patient data set comprising an X-ray image and a paired CT image; s2, a spatial dimension expansion generation model from the x-ray image to the CT image is constructed, and the spatial dimension expansion generation model comprises an encoder, a spatial expansion module and a decoder; s3, training the spatial dimension expansion generation model in the step S2 by using the X-ray image acquired in the step S1 and the paired CT image, and adjusting the weight through iteration to minimize CT reconstruction loss and CT projection loss; and S4, training the basic sketching model by using the X-ray image acquired in the step S1 and the paired CT image. The problems that the current adaptive radiotherapy technology depends on a 3D image, the cost is high, and the time of fractional treatment is long can be solved.

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.

Among the various aspects of the present disclosure is the provision of methods and systems for segmenting images and expediting a contouring process for MRI-guided adaptive radiotherapy (MR-IGART) comprising applying a convolutional neural network (CNN), wherein the CNN accurately segments organs (e.g., the liver, kidneys, stomach, bowel, or duodenum) in 3D MR images.

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.

The invention discloses a multi-leaf collimator prediction method and system and a computing device, and belongs to the technical field of medical care informatics, and the multi-leaf collimator prediction method comprises the following steps: obtaining a radiotherapy plan based on volume intensity modulated radiotherapy; encoding the radiotherapy plan and the multi-leaf grating through an encoder to obtain a second vector; projecting the second vector based on submerged space rotation to obtain a projection vector; decoding the projection vector through a decoder of a control point to obtain a prediction vector; and obtaining a motion sequence according to the prediction vector. A radiotherapy plan is converted into a motion sequence and an executable plan, so that time waste and efficiency loss caused by blade serialization are effectively avoided, and more efficient execution plan generation and adaptive radiotherapy can be realized.