m+Medelevator.RU
← Поиск публикаций
Medical image analysis · 2026

MRIgRT real-time target tracking: TrackRAD2025 challenge report.

СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Blöcker Tom Julius, Görts Pia A W, Wang Yiling, Lombardo Elia, Thummerer Adrian, Fan Yu, Zhao Yue, Papadopoulou Christianna Iris, Hurkmans Coen, Tijssen Rob H N, Cusumano Davide, Intven Martijn Pw, Borman Pim, Riboldi Marco, Dudáš Denis, Byrne Hilary L, Placidi Lorenzo, Fusella Marco, Jameson Michael, Palacios Miguel A, Cobussen Paul, Finazzi Tobias, Tetar Shyama U, Haasbeek Cornelis J A, Keall Paul, Maspero Matteo, Kurz Christopher, Tarifa Amparo Soeli Betancourt, He Kailin, Zhu Shengqian, Song Ying, Li Guangjun, Hu Junjie, Knispel Felix, Gatidis Sergios, Chu Hung, Guo Jiapan, Nielsen Maximilian, Sentker Thilo, Boussot Valentin, Hémon Cédric, Ni Jing, Georgas Konstantinos, Vagenas Theodoros P, Matsopoulos George K, Landry Guillaume

Аннотация

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.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты