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US 2023/0402152 A1Published application
Dose-directed radiation therapy plan generation using computer modeling techniques
AssigneeVarian Medical Systems, Inc.
InventorsSimeng Zhu, Alexander E. Maslowski, Esa Kuusela
Life of this patent
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Abstract
Provided herein are methods and systems to train and execute a computer model that uses artificial intelligence methodologies (e.g., deep learning) to learn and predict Multi-leaf Collimator (MLC) openings and control weights for a radiation therapy treatment plan.
Claims20
A method comprising:
- receiving, by a processor, treatment objectives for a patient including at least a dose-volume for at least one structure of the patient to be treated via a radiation therapy machine;
- executing, by the processor, an artificial intelligence model to predict an attribute of a Multi-Leaf Collimator (MLC) opening and a corresponding movement attribute of an accelerator of the radiation therapy machine,
- wherein the artificial intelligence model is trained via a training dataset that comprises training treatment objectives and attributes associated with previously performed radiation therapy treatments comprising at least actual or projected dose-volume for the treated patients and corresponding MLC opening positions;
- and
- presenting, by the processor, a predicted MLC opening position and a corresponding predicted movement attribute of the accelerator of the radiation therapy machine.
2 more elements
A computer system comprising:
- a server comprising a processor and a non-transitory computer-readable medium containing instructions that when executed by the processor causes the processor to perform operations comprising:
- receiving treatment objectives for a patient including at least a dose-volume for at least one structure of the patient to be treated via a radiation therapy machine;
- executing an artificial intelligence model to predict an attribute of a Multi-Leaf Collimator (MLC) opening and a corresponding movement attribute of an accelerator of the radiation therapy machine,
- wherein the artificial intelligence model is trained via a training dataset that comprises training treatment objectives and attributes associated with previously performed radiation therapy treatments comprising at least actual or projected dose-volume for the treated patients and corresponding MLC opening positions;
- and
- presenting a predicted MLC opening position and a corresponding predicted movement attribute of the accelerator of the radiation therapy machine.
3 more elements
A system comprising a server having one or more processors configured to:
- receive treatment objectives for a patient including at least a dose-volume for at least one structure of the patient to be treated via a radiation therapy machine;
- execute an artificial intelligence model to predict an attribute of a Multi-Leaf Collimator (MLC) opening and a corresponding movement attribute of an accelerator of the radiation therapy machine,
- wherein the artificial intelligence model is trained via a training dataset that comprises training treatment objectives and attributes associated with previously performed radiation therapy treatments comprising at least actual or projected dose-volume for the treated patients and corresponding MLC opening positions;
- and
- present a predicted MLC opening position and a corresponding predicted movement attribute of the accelerator of the radiation therapy machine.
2 more elements
Dependencies are inferred from the claim text.