Elekta, Inc.

Sami Hissoiny

Michel Moreau

Stefan Pencea

Systems and methods for generating a radiotherapy treatment plan using information about gantry angle-indexed dose (GAID) variation are discussed. An exemplary system can include an interface to receive a beam model for use in the radiation machine, and a processor that can determine, for the radiation machine, a GAID variation represented by a plurality of radiation doses at different gantry angles. The processor can determine a radiation treatment plan for the patient using the beam model and the GAID variation.

Dose management based on cryostat variation

Radiation treatment planning and administration can include a Monte Carlo computer simulation tool to simulate photo-generated electrons in tissue. In the simulation, electrons that have left tissue voxels and entered air voxels can be evaluated to identify electrons that are circling along a spiraling trajectory in the air voxels. After at least one full spiraling circumference or other specified distance has been traversed using a detailed electron transport model, a simpler linear ballistic motion model can be instituted. This speeds simulation while accurately accounting for spiraling electrons that re-enter tissue voxels.

Radiation treatment planning or administration electron modeling

Systems and methods can include training a deep convolutional neural network model to provide a beam model for a radiation machine, such as to deliver a radiation treatment dose to a subject. A method can include determining a range of parameter values for at least one parameter of a beam model corresponding to the radiation machine, generating a plurality of sets of beam model parameter values, wherein one or more individual sets of beam model parameter values can include a parameter value selected from the determined range of parameter values, providing a plurality of corresponding dose profiles respectively corresponding to respective individual sets beam model parameter values in the plurality of sets of beam model parameter values, and training the neural network model using the plurality of beam models and the corresponding dose profiles.

Determining parameters for a beam model of a radiation machine using deep convolutional neural networks

A system and method for simulating energy transport between particles and an object in radiotherapy is provided. A three-dimensional representation, having a plurality of voxels, is generated for a portion of a patient to be treated. A material index for each of the plurality of voxels is determined. The material index has an identifier for a first boundary material corresponding to a voxel and a percentage of the voxel associated with a second boundary material. The method further comprises simulating energy transport between a particle and the object by calculating an amount of energy deposited by a particle within the patient based on the material indices of the voxels and a list of boundary materials.

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Longueuil, Canada

Sami Hissoiny