Dose values are displayed. The dose values display the take-up of radiation by an examination volume to be expected during an irradiation. An examination volume is segmented in an image, and the dose values are assigned to areas of the surface of the examination volume. The surface of the examination volume is displayed as a plane such that the areas displayed as flat will be graphically encoded by the dose values assigned to the respective areas.

A method of generating a treatment plan for treating a patient with radiotherapy, the method includes obtaining a plurality of sample plans, which are generated by use of a knowledge base comprising historical treatment plans and patient data. The method also includes performing a multi-criteria optimization based on the plurality of sample plans to construct a Pareto frontier, where the plurality of sample plans are evaluated with at least two objectives measuring qualities of the plurality of sample plans such that treatment plans on the constructed Pareto frontier are Pareto optimal with respect to the objectives. The method further includes identifying a treatment plan by use of the constructed Pareto frontier.

A dose prediction model can be determined for generating a dose distribution of a treatment plan for irradiating a target structure within a patient. Treatment plans from previous patients can be analyzed to determine D characteristic values to obtain a D dimensional point for each treatment plan. The treatment plans can be clustered based on the D dimensional points. The treatment plans of a cluster can then be used to determine a dose prediction model. A dose prediction model for patient can be selected from among multiple models. Characteristics about the patient can be used to determine a D dimensional point corresponding to the patient. The D-dimensional point can be used to select a model in comparison to D dimensional points of the models.

An optimization method for ion based radiotherapy includes inverse planning based on optimization variables related to the particle energy, a range modulator or ridge filter, a block and/or a range compensator. This enables automatic optimization of complex cases.

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.

Systems and methods are provided for radiation delivery. An exemplary method includes receiving an image depicting anatomical data of a target region of patient tissue and determining an initial prescribed dose of radiotherapeutic radiation to be delivered to the target region. The method also includes discretizing the arc for VMAT into a plurality of arc segments and performing an iteration process for determining an arc dose according to radiation delivered in the arc segments. The method further includes determining whether a condition for terminating the iteration process is met and terminating the iteration process based on a result of the determination that the condition for terminating the iteration process is met.

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.