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arXiv · 2503.19119

TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy

Yiling Wang·Elia Lombardo·Adrian Thummerer·Tom Blöcker·Yu Fan·Yue Zhao·Christianna Iris Papadopoulou·Coen Hurkmans·Rob H. N. Tijssen·Pia A. W. Görts·Shyama U. Tetar·Davide Cusumano·Martijn P. W. Intven·Pim Borman·Marco Riboldi·Denis Dudáš·Hilary Byrne·Lorenzo Placidi·Marco Fusella·Michael Jameson·Miguel Palacios·Paul Cobussen·Tobias Finazzi·Cornelis J. A. Haasbeek·Paul Keall·Christopher Kurz·Guillaume Landry·Matteo Maspero
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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.

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Yiling Wang, Elia Lombardo, Adrian Thummerer, Tom Blöcker, Yu Fan, Yue Zhao, Christianna Iris Papadopoulou, Coen Hurkmans, Rob H. N. Tijssen, Pia A. W. Görts, Shyama U. Tetar, Davide Cusumano, Martijn P. W. Intven, Pim Borman, Marco Riboldi, Denis Dudáš, Hilary Byrne, Lorenzo Placidi, Marco Fusella, Michael Jameson, Miguel Palacios, Paul Cobussen, Tobias Finazzi, Cornelis J. A. Haasbeek, Paul Keall, Christopher Kurz, Guillaume Landry, Matteo Maspero. 2025-03-24. TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy. https://doi.org/10.1002/mp.17964

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