TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy
Abstract
Purpose: Magnetic resonance imaging (MRI) to visualize anatomical motion is becoming increasingly important when treating cancer patients with radiotherapy. Hybrid MRI-linear accelerator (MRI-linac) systems allow real-time motion management during irradiation. This paper presents a multi-institutional real-time MRI time series dataset from different MRI-linac vendors. The dataset is designed to support developing and evaluating real-time tumor localization (tracking) algorithms for MRI-guided radiotherapy within the TrackRAD2025 challenge (https://trackrad2025.grand-challenge.org/). Acquisition and validation methods: The dataset consists of sagittal 2D cine MRIs in 585 patients from six centers (3 Dutch, 1 German, 1 Australian, and 1 Chinese). Tumors in the thorax, abdomen, and pelvis acquired on two commercially available MRI-linacs (0.35 T and 1.5 T) were included. For 108 cases, irradiation targets or tracking surrogates were manually segmented on each temporal frame. The dataset was randomly split into a public training set of 527 cases (477 unlabeled and 50 labeled) and a private testing set of 58 cases (all labeled). Data Format and Usage Notes: The data is publicly available under the TrackRAD2025 collection: https://doi.org/10.57967/hf/4539. Both the images and segmentations for each patient are available in metadata format. Potential Applications: This novel clinical dataset will enable the development and evaluation of real-time tumor localization algorithms for MRI-guided radiotherapy. By enabling more accurate motion management and adaptive treatment strategies, this dataset has the potential to advance the field of radiotherapy significantly.
Keywords
Cite
@article{arxiv.2503.19119,
title = {TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy},
author = {Yiling Wang and Elia Lombardo and Adrian Thummerer and Tom Blöcker and Yu Fan and Yue Zhao and Christianna Iris Papadopoulou and Coen Hurkmans and Rob H. N. Tijssen and Pia A. W. Görts and Shyama U. Tetar and Davide Cusumano and Martijn P. W. Intven and Pim Borman and Marco Riboldi and Denis Dudáš and Hilary Byrne and Lorenzo Placidi and Marco Fusella and Michael Jameson and Miguel Palacios and Paul Cobussen and Tobias Finazzi and Cornelis J. A. Haasbeek and Paul Keall and Christopher Kurz and Guillaume Landry and Matteo Maspero},
journal= {arXiv preprint arXiv:2503.19119},
year = {2025}
}
Comments
10 pages, 5 figures, 2 tables; submitted to Medical Physics, tentatively accepted