DoseRAD2026 Challenge dataset: AI accelerated photon and proton dose calculation for radiotherapy

2026-04-14

AI Summary

This work addresses the challenge of rapidly and accurately computing photon and proton dose distributions for MRI-guided and real-time adaptive radiotherapy by introducing a publicly available dataset comprising paired CT–MRI images and corresponding beam-level Monte Carlo dose calculations from 115 thoracic and abdominal patients. High-fidelity dose ground truths were generated using an open-source Monte Carlo algorithm, and the data underwent preprocessing—including deformable image registration, air-cavity correction, and resampling—before being standardized and released in MetaImage format with JSON-based beam configuration files. The dataset includes 40,500 photon and 81,000 proton beam dose samples, establishing the first large-scale benchmark supporting MRI-only radiotherapy workflows and providing a rigorous platform for training and evaluating AI-driven fast dose calculation methods.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Multi-modal Vision
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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 Monte Carlo algorithms, yielding 40,500 photon beams and 81,000 proton beamlets. Data format and usage notes: Data are organized into photon and proton subsets with paired CT-MRI images, beam-level dose distributions, and JSON beam configuration files. Files are provided in compressed MetaImage (.mha) format. The dataset is released under CC BY-NC 4.0, with training data available from April 2026 and the test set withheld until March 2030. Potential applications: The dataset supports benchmarking of fast dose calculation methods, including beam-level dose estimation for photon and proton therapy, MRI-based dose calculation in MRI-guided workflows, and real-time adaptive radiotherapy.

Problem

• dose calculation
• radiotherapy
• MRI-guided
• photon therapy
• proton therapy

Innovation

• Monte Carlo dose calculation
• MRI-guided radiotherapy
• beam-level dose estimation
• public benchmark dataset
• real-time adaptive radiotherapy