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DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation.

Sci Rep 2020 07 6;10(1):11073. Epub 2020 Jul 6.

Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA.

Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the-art techniques rely on convolutional neural networks (CNNs) that use pixel-to-pixel loss to update network parameters. However, stereotactic body radiotherapy (SBRT) dose is often heterogeneous, making it difficult to model using pixel-level loss. Read More

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