  Fast and accurate dose predictions are one of the bottlenecks in treatment
planning for microbeam radiation therapy (MRT). In this paper, we propose a
machine learning (ML) model based on a 3D U-Net. Our approach predicts
separately the large doses of the narrow high intensity synchrotron microbeams
and the lower valley doses between them. For this purpose, a concept of macro
peak doses and macro valley doses is introduced, describing the respective
doses not on a microscopic level but as macroscopic quantities in larger
voxels. The ML model is trained to mimic full Monte Carlo (MC) data. Complex
physical effects such as polarization are therefore automatically taking into
account by the model.
  The macro dose distribution approach described in this study allows for
superimposing single microbeam predictions to a beam array field making it an
interesting candidate for treatment planning. It is shown that the proposed
approach can overcome a main obstacle with microbeam dose predictions by
predicting a full microbeam irradiation field in less than a minute while
maintaining reasonable accuracy.
