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) images obtained by a computed tomography (CT) system; receiving 3D planned dose images, 3D organ segmentation contour images, and radiotherapy plans generated by a treatment planning system (TPS); processing all 3D images to have the same spatial resolution and matrix size; processing 3D CT images to convert image intensity to density; 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 particles' inplane and crossplane positions reverse transported from the phase-space surface back to the treatment head; calculating the Gaussian means and standard deviations of particles' positions at the treatment head and determining the criteria for particle source; calculating the probabilities for each particle source; calculating the probabilities for the medical linear accelerator treatment head to produce different particle species; binning the inplane position probability information of particles into a single histogram for each source and particle species; binning the crossplane position probability information of particles into histograms for each bin of the inplane position histogram for each source and particle species; binning the inplane direction cosine probability information of particles into histograms for each bin of the inplane position histogram for each source and particle species; binning the crossplane direction cosine probability information of particles into histograms for each bin of the crossplane position histogram for each source and particle species; binning the kinetic energy probability information of particles into radially binned histograms for each source and 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 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 CT densities according to radiotherapy plans to produce 3D images of simulated patient dose;

A computer-implemented method for generating a radiation therapy treatment plan for a volume of a patient, the method comprising: receiving an image of the volume; receiving at least one dose-distribution-derived function configured to provide a value as an output based on, as input, at least part of a dose distribution defined relative to said image; receiving a first probability distribution and at least a second, different, probability distribution, the first and at least second probability distributions; defining a multi-criteria optimization problem comprising at least a first objective function based on the at least one dose-distribution-derived function, the first probability distribution and a loss function; and a second objective function based on the at least one dose-distribution-derived function, the second probability distribution and the loss function; and performing a multi-criteria optimization process based on said at least two objective functions to generate at least two output treatment plans.

These teachings provide for quickly yet accurately forming an influence matrix by generating the influence matrix via integration with a Monte Carlo particle transport simulation. The resultant influence matrix can then be utilized in an ordinary manner when optimizing a radiation treatment plan. By one approach, the foregoing comprises generating the influence matrix via integration with a Monte Carlo particle transport simulation on a particle-by-particle basis. For example, for each particle, these teachings can provide for identifying a spot to which the particle belongs and then adding a dose deposited by the particle during transport to an influence matrix element that corresponds to a spot to which the particle belongs and a voxel to where the dose was deposited.

A method of determining treatment geometries for a radiotherapy treatment includes providing a patient model having one or more regions of interest (ROIs); defining a delivery coordinate space (DCS); for each beam's eye view (BEV) plane of each vertex in the DCS, and for each ROI, evaluating a dose of the ROI using transport solutions; evaluating a BEV scores of each pixel of the BEV plane using the doses of the one or more ROIs; determining one or more BEV regions in the BEV plane based on the BEV scores; determining a BEV region connectivity manifold based on the BEV regions; determining a set of treatment trajectories based on the BEV region connectivity manifold; and determining one or more IMRT fields. Each treatment trajectory defines a path through a set of vertices in the DCS. Each IMRT field defines a direction of incidence corresponding to a vertex in the DCS.

Apparatus for planning a diffusing alpha-emitter radiation therapy (DaRT) treatment session. The apparatus includes an output interface and a memory configured with a plurality of tables which provide an accumulated measure of radiation over a specific time period, due to one or more types of DaRT radiotherapy sources which emit daughter radionuclides from the source, for a plurality of different distances and angles relative to the DaRT radiotherapy source. In addition, a processor is configured to receive a description of a layout of a plurality of DaRT radiotherapy sources in a tumor, to calculate a radiation dose distribution in the tumor responsive to the layout, using the tables in the memory, and to output feedback for the treatment responsive to the radiation dose distribution, through the output interface.

Systems and methods for a pre-treatment quality assurance (QA) of a radiotherapy device may be provided. The method may include determining a measured dose image through an electronic portal dose imaging device (EPID). The method may include determining an energy fluence distribution map related to radiation beams predicted by a first portal dose prediction model. The method may include determining a predicted dose image based on the energy fluence distribution map and a simulated energy response curve related to the EPID. The method may further include determining differences between the measured and predicted dose images by comparing the dose distributions of the measured and predicted dose images.

A system for generating a dose distribution is provided. The system may obtain a first dose distribution in at least a portion of a subject. The system may also obtain a trained machine learning model. The system may further generate, based on the first dose distribution and the trained machine learning model, a second dose distribution in the at least a portion of the subject, wherein the second dose distribution has a higher accuracy than that of the first dose distribution.

The disclosure is related to an apparatus and method for charged hadron therapy verification. The apparatus comprises a collimator comprising a plurality of collimator slabs of a given thickness, spaced apart so as to form an array of mutually slit-shaped openings, configured to be placed at a right angle to the beam line, so as to allow the passage of prompt gammas from the target, the collimator being defined at least by three geometrical parameters being the width and depth of the slit-shaped openings and a fill factor. The disclosure is also related to a method for charged hadron therapy verification with a multi-slit camera.

A radiation therapy planning apparatus performs dose calculation at high speed and with high accuracy for radiation therapy in a scanning irradiation method. The apparatus includes a display, an arithmetic processing apparatus, a memory, and a data server, which is connected to a particle beam irradiation apparatus. A dose calculation unit of the arithmetic processing apparatus calculates dose distribution by a simplified Monte Carlo algorithm, and corrects the dose distribution by a decreasing rate stored in a particle number decreasing rate table of the memory, and stores the corrected dose distribution in an integrated dose distribution table. By using the simplified Monte Carlo algorithm and the particle number decreasing rate that corrects the simplified Monte Carlo algorithm, the dose distribution is calculated, and thereby, it is possible to realize a radiation therapy planning apparatus that performs dose calculation at a high speed with high accuracy.

Disclosed herein are systems, methods, and computer-readable storage devices for manufacturing patient-specific bolus for use in targeted radiotherapy treatment. Based on dose calculations without a bolus and based on three-dimensional scan data of a patient, the example system generates a model of a bolus for targeting radiotherapy treatment to a planning target volume or target region within the patient. The system can perform several iterations to generate a resulting model for the bolus. Then, the system can generate instructions for controlling a three-dimensional printer to generate the bolus that conforms to the patient's skin surface while also specifically targeting the planning target volume for the radiotherapy treatment. In this way, the amount of radiotherapy treatment administered to other tissue is reduced, while the costs, time, and human involvement in creating the bolus are significantly reduced.