Treatment plan prediction for lung IMRT using deep learning based fluence map generation.
Journal:
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date:
May 21, 2022
Abstract
PURPOSE: Recently, it has been shown that automated treatment planning can be executed by direct fluence prediction from patient anatomy using convolutional neural networks. Proof of principle publications utilise a fixed dose prescription and fixed collimator (0°) and gantry angles. The goal of this work is to further develop these principles for the challenging lung cancer indication with variable dose prescriptions, collimator and gantry angles. First we investigate the impact of clinical applicable collimator angles and various input parameters. Then, the model is tested in a complete user independent planning workflow.