MRIgRT real-time target tracking: TrackRAD2025 challenge report
Year: 2026
Type: article
Abstract: Magnetic resonance imaging (MRI)-guided radiotherapy (MRIgRT) integrates MRI with linear accelerators (MRI-linacs), enabling real-time motion management based on temporally resolved 2D MRI (cine-MRI). Current systems rely on template matching or deformable image registration for radiotherapy target (typically the gross tumor volume) localization, which allows beam gating. Further advances in localization could support more precise and efficient delivery methods. https://trackrad2025.grand-challenge.org/ was organized to provide a common dataset to benchmark algorithms for MRIgRT target tracking in 2D+t cine-MRI. Participants propagated target segmentation masks from an initialization frame across subsequent frames. The dataset comprised sagittal cine-MRI scans of 585 cancer patients undergoing radiotherapy at 0.35 T and 1.5 T MRI-linacs at six different institutions, with expert-annotated targets in 108 sequences. Target sites included the thorax (179 cases), abdomen (266 cases), and pelvis (140 cases). A total of 477 unlabeled and 50 labeled cases were provided for training purposes, 58 cases were kept private for preliminary testing (8) and final evaluation (50). The algorithms submitted by participants were executed on the challenge platform and assessed using metrics in three categories: geometric accuracy, surrogate dose accuracy and execution speed. Rankings were derived via a Rank-Then-Mean scheme. TrackRAD2025 attracted 148 registrations from 28 countries, 100 preliminary submissions and 24 final submissions from 14 teams. The top five methods achieved mean Dice similarity coefficients >0.87 and Euclidean center distances <2.1 mm, comparable to interobserver variability. Leading top five solutions featured foundation models with (4) or without (1) finetuning. Field strength had minimal effect on performance and tracking worked better for the pelvis with reduced motion amplitude compared to the thorax and abdomen cases, which achieved equivalent performance. TrackRAD2025 established a benchmark for MRIgRT tracking on multi-institutional cine-MRI data, highlighting foundation models as promising for clinical translation.
Source: Medical Image Analysis
Authors: Tom Julius Blöcker, Pia A.W. Görts, Yiling Wang, Elia Lombardo, Adrian Thummerer, Yu Fan, Yue Zhao, Christianna Iris Papadopoulou, Coen Hurkmans, Rob H.N. Tijssen, Davide Cusumano, Martijn PW Intven, Pim Borman, Marco Riboldi, Denis Dudáš, Hilary L. Byrne, Lorenzo Placidi, Marco Fusella, Michael Jameson, Miguel A. Palacios, Paul Cobussen, Tobias Finazzi, Shyama U. Tetar, Cornelis J.A. Haasbeek, Paul Keall, Matteo Maspero, Christopher Kurz, Amparo Soeli Betancourt Tarifa, Kailin He, Shengqian Zhu, Ying Song, Guangjun Li, Junjie Hu, Felix Knispel, Sergios Gatidis, Hung Chu, Jiapan Guo, Maximilian Nielsen, Thilo Sentker, Valentin Boussot, Cédric Hémon, Jing Ni, Konstantinos Georgas, Theodoros P. Vagenas, George K. Matsopoulos, Guillaume Landry
Institutions: LMU Klinikum, Ludwig-Maximilians-Universität München, Catharina Ziekenhuis, Eindhoven University of Technology, University of Electronic Science and Technology of China, Sichuan Cancer Hospital, University Medical Center Utrecht, Czech Technical University in Prague, The University of Sydney, St Vincent's Hospital Sydney, GenesisCare, Agostino Gemelli University Polyclinic, Istituti di Ricovero e Cura a Carattere Scientifico, Policlinico Abano Terme, Amsterdam UMC Location Vrije Universiteit Amsterdam, Utrecht University, Radboud University Nijmegen, Radboud University Medical Center, Sichuan University, West China Medical Center of Sichuan University, West China Hospital of Sichuan University, RWTH Aachen University, Stanford University, University of Groningen, University Medical Center Hamburg-Eppendorf, Inserm, Laboratoire Traitement du Signal et de l'Image, Université Rennes 1, Université de Rennes, ShanghaiTech University, United Imaging Healthcare (China), National Technical University of Athens
Language: English
Subfield: Radiation
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,170
Award: G847847918
OpenAlex ID: W7162196583