Work

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

Year: 2026

Type: article

Abstract: 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. DATA FORMAT AND USAGE NOTES: 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. POTENTIAL APPLICATIONS: 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.

Source: Medical Physics

DOI: 10.1002/mp.70714