DoseRAD2026 Challenge dataset: AI accelerated photon and proton dose calculation for radiotherapy
Fan Xiao, Nikolaos Delopoulos, Niklas Wahl, Lennart Volz, Lina Bucher, Matteo Maspero, Miguel Palacios, Muheng Li, Samir Schulz, Viktor Rogowski, Ye Zhang, Zoltan Perko, Christopher Kurz, George Dedes, Guillaume Landry, Adrian Thummerer

TL;DR
The DoseRAD2026 dataset and challenge provide a comprehensive benchmark for developing and evaluating fast, accurate dose calculation methods in radiotherapy using paired CT and MRI data with Monte Carlo dose distributions.
Contribution
This work introduces a large, publicly available dataset with paired imaging and dose data for photon and proton therapy, supporting advanced dose calculation method development.
Findings
Dataset includes 115 patient cases with paired CT and MRI.
Ground-truth dose distributions computed via Monte Carlo simulations.
Supports benchmarking of MRI-based and real-time adaptive dose calculation methods.
Abstract
Purpose: Accurate dose calculation is essential in radiotherapy for precise tumor irradiation while sparing healthy tissue. With the growing adoption of MRI-guided and real-time adaptive radiotherapy, fast and accurate dose calculation on CT and MRI is increasingly needed. The DoseRAD2026 dataset and challenge provide a public benchmark of paired CT and MRI data with beam-level photon and proton Monte Carlo dose distributions for developing and evaluating advanced dose calculation methods. Acquisition and validation methods: The dataset comprises paired CT and MRI from 115 patients (75 training, 40 testing) treated on an MRI-linac for thoracic or abdominal lesions, derived from the SynthRAD2025 dataset. Pre-processing included deformable image registration, air-cavity correction, and resampling. Ground-truth photon (6 MV) and proton dose distributions were computed using open-source…
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