DoseRAD2026: A benchmark dataset for AI-accelerated photon and proton dose calculation in radiotherapy.
Authors
Affiliations (18)
Affiliations (18)
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany.
- Biophysics Department, GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany.
- Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany.
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Baden-Württemberg, Germany.
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands.
- Department of Radiation Oncology, Amsterdam UMC location Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, Netherlands.
- Center for Proton Therapy, Paul Scherrer Institute (PSI), Villigen, Switzerland.
- Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany.
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.
- Department of Radiation Science and Technology, Delft University of Technology, Delft, the Netherlands.
- Radformation Inc., New York, USA.
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Munich, Germany.
- German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and LMU University Hospital, Munich, Germany.
- Bavarian Cancer Research Center (BZKF), Munich, Germany.
- Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.
- Department of Digital Medicine, Faculty of Medicine, University of Bern, Bern, Switzerland.
Abstract
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. 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. The dataset is organized into photon and proton subsets, each comprising paired CT-MRI images, beam-level dose distributions, and JSON files specifying patient-specific beam configurations. Data are provided in compressed MetaImage (.mha) format with standardized naming linking dose files to beam parameters in the JSON files. The dataset is released under a CC BY-NC 4.0 license, with the training set available from April 2026 and the test set withheld until March 2030. The DoseRAD2026 dataset enables benchmarking and development of fast dose calculation methods for radiotherapy applications. Potential use cases include rapid beam-level dose estimation for photon and proton therapy, MRI-based dose calculation in MRI-only and MRI-guided radiotherapy workflows, and support for real-time adaptive radiotherapy strategies.