TrackRAD2025 challenge dataset: real‐time tumor tracking for MRI‐guided radiotherapy
Yiling Wang, Elia Lombardo, Adrian Thummerer, T. Blöcker, Fan Yu, Yue Zhao, Cristina I. Papadopoulou, Coen Hurkmans, Rob H.N. Tijssen, Pia A. W. Görts, S. Tetar, Davide Cusumano, Martijn Intven, Pim Borman, Marco Riboldi, Denis Dudáš, Hilary L. Byrne, Lorenzo Placidi, M. Fusella, Michael Jameson, M. Palacios, Paul Cobussen, Tobias Finazzi, Cornelis J.A. Haasbeek, Paul Keall, Christopher Kurz, Guillaume Landry, Matteo Maspero
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 (20-20543 frames per scan) 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.