arXiv 2503.191192025-05-23physics.med-phcs.CV

TrackRAD2025挑战数据集：用于MRI引导放射治疗的实时肿瘤追踪

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

Sichuan Cancer Hospital & Institute（四川省肿瘤医院研究所）；University of Electronic Science and Technology of China（电子科技大学）；LMU University Hospital（LMU大学医院）；LMU Munich（慕尼黑大学）；Catharina Hospital（凯瑟琳医院）；Mater Olbia Hospital（奥尔比亚 Mater 医院）；University Medical Center Utrecht（乌得勒支大学医学中心）；Ludwig-Maximilians-Universität München（慕尼黑大学）；Czech Technical University in Prague（布拉格捷克理工大学）；University of Sydney（悉尼大学）；Fondazione Policlinico Universitario Agostino Gemelli, IRCCS（阿戈斯蒂诺·杰梅利大学综合医院基金会 IRCCS）；Abano Terme Hospital（阿巴诺泰尔梅医院）；GenesisCare；St Vincent’s Hospital（圣文森特医院）；Amsterdam UMC location Vrije Universiteit Amsterdam（阿姆斯特丹自由大学医学中心）；Technical University Eindhoven（埃因霍温理工大学）

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, S… 展开作者

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

备注：: 10 pages, 5 figures, 2 tables; submitted to Medical Physics, tentatively accepted
DOI：: 10.1002/mp.17964

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AI总结：

本文提出TrackRAD2025多机构实时MRI时间序列数据集，包含585例MRI-linac病例，用于开发和评估MRI引导放射治疗中的实时肿瘤追踪算法。

摘要：

目的：在放射治疗癌症患者时，利用磁共振成像（MRI）可视化解剖运动正变得越来越重要。混合MRI直线加速器（MRI-linac）系统能够在照射过程中进行实时运动管理。本文提出了一个来自不同MRI-linac供应商的多机构实时MRI时间序列数据集。该数据集旨在支持TrackRAD2025挑战（https://trackrad2025.grand-challenge.org/）中MRI引导放射治疗的实时肿瘤定位（追踪）算法的开发和评估。采集与验证方法：该数据集包含来自六个中心（3个荷兰、1个德国、1个澳大利亚和1个中国）的585例患者的矢状位2D电影MRI。纳入了在两种商用MRI-linac（0.35 T和1.5 T）上采集的胸部、腹部和盆腔肿瘤。对于108例病例，在每个时间帧上手动分割了照射靶区或追踪替代物。数据集被随机分为527例的公开训练集（477例未标注和50例已标注）和58例的私有测试集（全部已标注）。数据格式与使用说明：该数据在TrackRAD2025集合下公开可用：https://doi.org/10.57967/hf/4539。每位患者的图像和分割结果均以元数据格式提供。潜在应用：这一新颖的临床数据集将促进MRI引导放射治疗中实时肿瘤定位算法的开发和评估。通过实现更精确的运动管理和自适应治疗策略，该数据集有潜力显著推动放射治疗领域的发展。

英文摘要：

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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