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"Fast and accurate dose predictions for novel radiotherapy treatments i" by Florian Mentzel, Kevin Kröninger et al.

Purpose: Novel radiotherapy techniques like synchrotron X-ray microbeam radiation therapy (MRT) require fast dose distribution predictions that are accurate at the sub-mm level, especially close to tissue/bone/air interfaces. Monte Carlo (MC) physics simulations are recognized to be one of the most accurate tools to predict the dose delivered in a target tissue but can be very time consuming and therefore prohibitive for treatment planning. Faster dose prediction algorithms are usually developed for clinically deployed treatments only. In this work, we explore a new approach for fast and accurate dose estimations suitable for novel treatments using digital phantoms used in preclinical development and modern machine learning techniques. We develop a generative adversarial network (GAN) model, which is able to emulate the equivalent Geant4 MC simulation with adequate accuracy and use it to predict the radiation dose delivered by a broad synchrotron beam to various phantoms. Methods: The ....

Monte Carlo , Deep Learning , Lose Prediction , Generative Adversarial Networks , Novel Treatments , Ynchrotron Radiation Therapy ,

What is Generative AI? IT Business Edge

Generative AI is a promising advancement in artificial intelligence. Here is what that means for enterprises large and small. ....

Generative Adversarial Networks Gans , Generative Adversarial Networks , What Does Explainable , Degenerative Art , Road Ahead ,