Saved in:
| Main Authors: | , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.12778 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910129217077248 |
|---|---|
| author | Xiao, Fan Delopoulos, Nikolaos Wahl, Niklas Volz, Lennart Bucher, Lina Maspero, Matteo Palacios, Miguel Li, Muheng Schulz, Samir Rogowski, Viktor Zhang, Ye Perko, Zoltan Kurz, Christopher Dedes, George Landry, Guillaume Thummerer, Adrian |
| author_facet | Xiao, Fan Delopoulos, Nikolaos Wahl, Niklas Volz, Lennart Bucher, Lina Maspero, Matteo Palacios, Miguel Li, Muheng Schulz, Samir Rogowski, Viktor Zhang, Ye Perko, Zoltan Kurz, Christopher Dedes, George Landry, Guillaume Thummerer, Adrian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12778 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DoseRAD2026 Challenge dataset: AI accelerated photon and proton dose calculation for radiotherapy Xiao, Fan Delopoulos, Nikolaos Wahl, Niklas Volz, Lennart Bucher, Lina Maspero, Matteo Palacios, Miguel Li, Muheng Schulz, Samir Rogowski, Viktor Zhang, Ye Perko, Zoltan Kurz, Christopher Dedes, George Landry, Guillaume Thummerer, Adrian Medical Physics Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | DoseRAD2026 Challenge dataset: AI accelerated photon and proton dose calculation for radiotherapy |
| topic | Medical Physics Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.12778 |