8 results about "Planned Dose" 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 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.

This specification discloses a dose verification method, apparatus, storage medium, and electronic device, relating to the field of radiotherapy technology. It can acquire target distance, equipment distance, detector convolution kernel, CT correction factor, detector dose data, and irradiation distances at multiple dose accumulation points. Based on the dose data at each coordinate point and the detector scattering convolution kernel, the initial deposited dose corresponding to each target point is determined. Then, based on the equipment distance, target distance, and the initial deposited dose at each coordinate point, the output beam intensity of the radiation device is determined. Based on the output beam intensity, initial deposited dose, equipment distance, CT correction factor, and irradiation distances at multiple dose accumulation points, the dose distribution at each dose accumulation point is determined. Finally, the dose distribution is compared and verified with the planned dose distribution. It is evident that the dose verification process considers the influence of various factors such as radiation device performance and detector position, thus improving the accuracy of dose verification.

The invention provides a medical linear accelerator machine room radiation dose evaluation method and system. The method comprises the following steps: acquiring a radiotherapy plan through a DICOM-RT protocol, constructing a model based on computer room CAD parameters, converting structures such as a primary / secondary shielding body, a rosemary channel and the like into an ROI region containing material density, and fusing the ROI region with a patient CT image to form three-dimensional density distribution. After a dose calculation grid is generated, a radiotherapy plan dose algorithm is used to accumulatively calculate dose distribution after execution of all treatment plans, a dose value of a key shielding point POI and a dose volume histogram of an ROI area are mainly analyzed, and finally radiation protection efficiency is evaluated through a dose threshold value. The system comprises four modules: a receiving module for acquiring treatment data, a sketching module for establishing a machine room model, a calculation module for completing dose distribution superposition, and an evaluation module for judging a shielding effect. According to the method, a radiotherapy plan dose algorithm is innovatively applied to large-scale protection evaluation of the machine room, and quantitative analysis of radiation safety of the machine room is realized.

The invention provides a radiotherapy dose real-time monitoring system, which comprises a data interface module for acquiring multi-source data related to radiotherapy in real time, the multi-source data comprises plan data imported from a treatment plan system, and the plan data comprises three-dimensional plan dose distribution and plan anatomical structure data; according to the invention, the real-time dynamic monitoring of the radiation treatment dose is realized, the limitation that only pre-verification before treatment or offline analysis after treatment can be carried out in the traditional technology is broken through, the treatment plan data, the machine operation parameters and the real-time anatomical image of the patient are integrated, and the real-time monitoring of the radiation treatment dose is realized. A multi-source data fusion processing mechanism is innovatively constructed, the comprehensive influence of machine execution deviation and anatomical change of a patient on the dose is quantified in real time, actual dose distribution in a body is dynamically reconstructed, dose delivery is changed from an invisible black box state to a real-time traceable visual process, and accurate monitoring of the radiotherapy dose in treatment is truly realized.

The present disclosure relates to a method and apparatus for fast Monte Carlo (MC) dose calculation using a virtual source model (VSM). The method includes: receiving three-dimensional (3D) CT images obtained by a CT system; receiving 3D planned dose images, 3D organ segmentation contour images, and radiotherapy plans generated by a treatment planning system (TPS); processing 3D CT images, 3D planned dose images, 3D organ segmentation contour images to have the same spatial resolution and matrix size; further processing 3D CT images to convert image intensity to 3D density maps; processing the radiotherapy plans to generate instructions on how to simulate plan delivery; building VSM using inverse cumulative density function (CDF) tables for the simulation of radiotherapy plans, wherein the step of building VSM comprises: receiving output data files containing phase-space information for the radiation output of a specific medical linear accelerator treatment head; calculating the probability of the inplane and crossplane positions of the radiation particles reverse transported from the phase-space surface back to the treatment head; calculating the Gaussian means and standard deviations of the radiation particles' positions at the treatment head; calculating the probabilities for the source of each radiation particle; calculating the probabilities for the medical linear accelerator treatment head to produce different radiation particle species; binning the inplane position probability information of radiation particles into a single histogram for each source and radiation particle species; binning the crossplane position probability information of radiation particles into histograms for each bin of the inplane position histogram for each source and radiation particle species; binning the inplane direction cosine probability information of radiation particles into histograms for each bin of the inplane position histogram for each source and radiation particle species; binning the crossplane direction cosine probability information of radiation particles into histograms for each bin of the crossplane position histogram for each source and radiation particle species; binning the kinetic energy probability information of radiation particles into radially binned histograms for each source and radiation particle species; converting probability densities for inplane and crossplane positions, inplane and crossplane direction cosines, and kinetic energies histograms into cumulative probability densities for each source and radiation particle species; and inverting cumulative probability densities and converting into probability binned inverse CDF tables; simulating and transporting external beams using VSM through virtual treatment machines to the 3D density maps according to radiotherapy plans to produce 3D simulated dose images; and post-processing the 3D planned dose images, 3D organ segmentation contour images, and the 3D simulated dose images to obtain a final report comparing planned versus simulated dose images.

The present disclosure describes a method for verifying whether a radiotherapy device faithfully executes a treatment plan, a radiotherapy device, an electronic device, and a computer-readable storage medium. The method comprises: adopting a treatment plan, the treatment plan comprising a plurality of control points; setting a time period between two adjacent control points as a verification time period; pre-calculating a planned dose distribution that should be delivered by the radiotherapy device corresponding to each control point; calculating, according to an operating parameter of the radiotherapy device, an implemented dose distribution during a verification time period formed by a current control point and a previous control point adjacent to the current control point; comparing a difference between the planned dose distribution and the implemented dose distribution during the verification time period; and determining, on the basis of the difference, whether the treatment plan has been faithfully executed. According to the present disclosure, the execution condition of the radiotherapy device can be continuously verified in each verification time period, enabling real-time determination of whether the radiotherapy device has faithfully executed the treatment plan.

The invention discloses a radiation treatment plan dose evaluation method and system based on intelligent prediction, and the method comprises the steps: obtaining a treatment scheme formulated by a radiation treatment plan system, and carrying out the calculation to obtain corresponding plan dose distribution data Dose-A; utilizing the trained neural network model S-NET to predict the actual beam dose rate of each sub-field; utilizing the trained neural network model M-NET to predict the actual position of each leaf of the grating in each sub-field; based on the predicted actual beam dose rate of each sub-field and the actual position of each blade of the grating in each sub-field, performing dose calculation to obtain predicted actual dose distribution data Dose-B; calculating first deviation data DELTA-A between the planned dose distribution data Dose-A and the predicted actual dose distribution data Dose-B; calculating second deviation data DELTA-P between the predicted dose distribution data Dose-B and a prescription dose; and generating a dose evaluation report based on the first deviation data DELTA-A and the second deviation data DELTA-P.