Medical physics · 2025
TrackRAD2025 challenge dataset: real-time tumor tracking for MRI-guided radiotherapy.
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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 Pw, 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
Аннотация
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.
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.