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Fast and accurate dose predictions are one of the bottlenecks in treatmentplanning for microbeam radiation therapy (MRT). In this paper, we propose amachine learning (ML) model based on a 3D U-Net. Our approach predictsseparately the large doses of the narrow high intensity synchrotron microbeamsand the lower valley doses between them. For this purpose, a concept of macropeak doses and macro valley doses is introduced, describing the respectivedoses not on a microscopic level but as macroscopic quantities in largervoxels. The ML model is trained to mimic full Monte Carlo (MC) data. Complexphysical effects such as polarization are therefore automatically taking intoaccount by the model.  The macro dose distribution approach described in this study allows forsuperimposing single microbeam predictions to a beam array field making it aninteresting candidate for treatment planning. It is shown that the proposedapproach can overcome a main obstacle with microbeam dose predictions bypredicting a full microbeam irradiation field in less than a minute whilemaintaining reasonable accuracy.

A step towards treatment planning for microbeam radiation therapy: fast peak and valley dose predictions with 3D U-Nets