Radiotherapy treatment plan modeling using generative adversarial networks

Abstract

Techniques for generating radiotherapy treatment plans and establishing machine learning models for the generation and optimization of radiotherapy dose data are disclosed. An example method for generating a radiotherapy dose distribution using a generative model, trained in a generative adversarial network, includes: receiving anatomical data of a human subject that indicates a mapping of an anatomical area for radiotherapy treatment; generating radiotherapy dose data corresponding to the mapping with use of the trained generative model, as the generative model processes the anatomical data as an input and provides the dose data as output; and identifying the radiotherapy dose distribution for the radiotherapy treatment of the human subject based on the dose data. Another example method for training of the generative model includes establishing values of the generative model and a discriminative model of the generative adversarial network using adversarial training, including in a conditional generative adversarial network arrangement.

Description

[0001] priority claim [0002] This application claims the benefit of priority of US Application Serial No. 15 / 966,228, filed April 30, 2018, which is hereby incorporated by reference in its entirety. technical field [0003] Embodiments of the present disclosure relate generally to medical data and artificial intelligence processing techniques. In particular, the present disclosure relates to the generation and use of data models in generative adversarial networks suitable for use with radiation therapy treatment planning workflows and system operations. Background technique [0004] Intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) have become the standard of care in modern cancer radiotherapy. Treatment planning for these and other forms of radiation therapy involves tailoring the specific amount of radiation exposure to the particular patient to be treated, as key organs are identified and target volumes are identified for treatment. M...

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Problems solved by technology

However, none of these methods provide or generate a detailed treatment planning model independent of either planning process

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