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000302844 037__ $$aDKFZ-2025-01384
000302844 041__ $$aEnglish
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000302844 1001_ $$aWang, Yiling$$b0
000302844 245__ $$aTrackRAD2025 challenge dataset: real-time tumor tracking for MRI-guided radiotherapy.
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000302844 520__ $$aMagnetic 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.
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000302844 650_7 $$2Other$$aMRI‐guided radiotherapy
000302844 650_7 $$2Other$$aReal‐time tumor localization
000302844 650_7 $$2Other$$aTrackRAD2025
000302844 650_2 $$2MeSH$$aRadiotherapy, Image-Guided: methods
000302844 650_2 $$2MeSH$$aHumans
000302844 650_2 $$2MeSH$$aMagnetic Resonance Imaging
000302844 650_2 $$2MeSH$$aNeoplasms: diagnostic imaging
000302844 650_2 $$2MeSH$$aNeoplasms: radiotherapy
000302844 650_2 $$2MeSH$$aTime Factors
000302844 650_2 $$2MeSH$$aDatabases, Factual
000302844 650_2 $$2MeSH$$aImage Processing, Computer-Assisted
000302844 7001_ $$aLombardo, Elia$$b1
000302844 7001_ $$aThummerer, Adrian$$b2
000302844 7001_ $$aBlöcker, Tom$$b3
000302844 7001_ $$aFan, Yu$$b4
000302844 7001_ $$aZhao, Yue$$b5
000302844 7001_ $$aPapadopoulou, Christianna Iris$$b6
000302844 7001_ $$aHurkmans, Coen$$b7
000302844 7001_ $$aTijssen, Rob H N$$b8
000302844 7001_ $$aGörts, Pia A W$$b9
000302844 7001_ $$aTetar, Shyama U$$b10
000302844 7001_ $$aCusumano, Davide$$b11
000302844 7001_ $$aIntven, Martijn Pw$$b12
000302844 7001_ $$aBorman, Pim$$b13
000302844 7001_ $$aRiboldi, Marco$$b14
000302844 7001_ $$aDudáš, Denis$$b15
000302844 7001_ $$aByrne, Hilary$$b16
000302844 7001_ $$aPlacidi, Lorenzo$$b17
000302844 7001_ $$aFusella, Marco$$b18
000302844 7001_ $$aJameson, Michael$$b19
000302844 7001_ $$aPalacios, Miguel$$b20
000302844 7001_ $$aCobussen, Paul$$b21
000302844 7001_ $$aFinazzi, Tobias$$b22
000302844 7001_ $$aHaasbeek, Cornelis J A$$b23
000302844 7001_ $$aKeall, Paul$$b24
000302844 7001_ $$aKurz, Christopher$$b25
000302844 7001_ $$0P:(DE-HGF)0$$aLandry, Guillaume$$b26
000302844 7001_ $$aMaspero, Matteo$$b27
000302844 773__ $$0PERI:(DE-600)1466421-5$$a10.1002/mp.17964$$gVol. 52, no. 7, p. e17964$$n7$$pe17964$$tMedical physics$$v52$$x0094-2405$$y2025
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