%0 Journal Article
%A Wang, Yiling
%A Lombardo, Elia
%A Thummerer, Adrian
%A Blöcker, Tom
%A Fan, Yu
%A Zhao, Yue
%A Papadopoulou, Christianna Iris
%A Hurkmans, Coen
%A Tijssen, Rob H N
%A Görts, Pia A W
%A Tetar, Shyama U
%A Cusumano, Davide
%A Intven, Martijn Pw
%A Borman, Pim
%A Riboldi, Marco
%A Dudáš, Denis
%A Byrne, Hilary
%A Placidi, Lorenzo
%A Fusella, Marco
%A Jameson, Michael
%A Palacios, Miguel
%A Cobussen, Paul
%A Finazzi, Tobias
%A Haasbeek, Cornelis J A
%A Keall, Paul
%A Kurz, Christopher
%A Landry, Guillaume
%A Maspero, Matteo
%T TrackRAD2025 challenge dataset: real-time tumor tracking for MRI-guided radiotherapy.
%J Medical physics
%V 52
%N 7
%@ 0094-2405
%C Hoboken, NJ
%I Wiley
%M DKFZ-2025-01384
%P e17964
%D 2025
%X 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/).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).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.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.
%K Radiotherapy, Image-Guided: methods
%K Humans
%K Magnetic Resonance Imaging
%K Neoplasms: diagnostic imaging
%K Neoplasms: radiotherapy
%K Time Factors
%K Databases, Factual
%K Image Processing, Computer-Assisted
%K MRI‐guided radiotherapy (Other)
%K Real‐time tumor localization (Other)
%K TrackRAD2025 (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:40660787
%R 10.1002/mp.17964
%U https://inrepo02.dkfz.de/record/302844