DoseRAD2026: A benchmark dataset for AI-accelerated photon and proton dose calculation in radiotherapy

Purpose: Accurate dose calculation is a fundamental component of modern radiotherapy, enabling precise tumor irradiation while sparing surrounding healthy tissue. With the increasing adoption of magnetic resonance imaging (MRI)-guided radiotherapy and growing interest in real-time adaptive radiotherapy, there is a demand for fast and accurate dose calculation techniques on both computed tomography (CT) and MRI. To facilitate research in this area, the DoseRAD2026 dataset and accompanying challenge introduce a publicly available benchmark dataset comprising paired CT and MRI data with corresponding beam-level photon and proton Monte Carlo (MC) dose distributions, enabling the development and systematic evaluation of advanced dose calculation and prediction methods.

Acquisition and validation methods: The DoseRAD2026 dataset comprises paired CT and MRI data from 115 patients (75 training, 40 testing) treated on a hybrid MRI-linear accelerator (MRI-linac) for thoracic or abdominal lesions and is derived from the SynthRAD2025 dataset. Image pre-processing included deformable image registration, air-cavity correction and resampling. Ground-truth photon (6 MV) and proton dose distributions were generated using open-source MC dose calculation algorithms and resulted in a training dataset of 40,500 photon beams and 81,000 proton beamlets.