TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy
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| Format: | Preprint |
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2025
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| author | Wang, Yiling Lombardo, Elia Thummerer, Adrian Blöcker, Tom Fan, Yu Zhao, Yue Papadopoulou, Christianna Iris Hurkmans, Coen Tijssen, Rob H. N. Görts, Pia A. W. Tetar, Shyama U. Cusumano, Davide Intven, Martijn P. W. Borman, Pim Riboldi, Marco Dudáš, Denis Byrne, Hilary Placidi, Lorenzo Fusella, Marco Jameson, Michael Palacios, Miguel Cobussen, Paul Finazzi, Tobias Haasbeek, Cornelis J. A. Keall, Paul Kurz, Christopher Landry, Guillaume Maspero, Matteo |
| author_facet | Wang, Yiling Lombardo, Elia Thummerer, Adrian Blöcker, Tom Fan, Yu Zhao, Yue Papadopoulou, Christianna Iris Hurkmans, Coen Tijssen, Rob H. N. Görts, Pia A. W. Tetar, Shyama U. Cusumano, Davide Intven, Martijn P. W. Borman, Pim Riboldi, Marco Dudáš, Denis Byrne, Hilary Placidi, Lorenzo Fusella, Marco Jameson, Michael Palacios, Miguel Cobussen, Paul Finazzi, Tobias Haasbeek, Cornelis J. A. Keall, Paul Kurz, Christopher Landry, Guillaume Maspero, Matteo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19119 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy Wang, Yiling Lombardo, Elia Thummerer, Adrian Blöcker, Tom Fan, Yu Zhao, Yue Papadopoulou, Christianna Iris Hurkmans, Coen Tijssen, Rob H. N. Görts, Pia A. W. Tetar, Shyama U. Cusumano, Davide Intven, Martijn P. W. Borman, Pim Riboldi, Marco Dudáš, Denis Byrne, Hilary Placidi, Lorenzo Fusella, Marco Jameson, Michael Palacios, Miguel Cobussen, Paul Finazzi, Tobias Haasbeek, Cornelis J. A. Keall, Paul Kurz, Christopher Landry, Guillaume Maspero, Matteo Medical Physics Computer Vision and Pattern Recognition 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. |
| title | TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy |
| topic | Medical Physics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.19119 |