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

2025-03-24

AI Summary

To address insufficient real-time tumor tracking accuracy in MRI-guided radiotherapy, this study introduces MRITrack-585—the first large-scale, multicenter, clinical-grade real-time MRI temporal dataset. It comprises cine MRI sequences from 585 patients with thoracic, abdominal, and pelvic tumors, acquired across multiple vendors and countries using both 0.35T and 1.5T MRI systems. The dataset features frame-wise manual segmentations of target volumes and tracking fiducials, organized in a standardized metadata schema, with clearly defined public training and private test splits. Hosted at DOI:10.57967/hf/4539, MRITrack-585 serves as the official benchmark for the TrackRAD2025 international challenge. It provides a critical resource for developing, fairly evaluating, and advancing real-time motion management and adaptive radiotherapy algorithms—enabling rigorous validation of segmentation, tracking, and motion prediction methods under clinically realistic conditions.

Technology Category

• Computer Vision: Motion & Tracking
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Intelligent Robots: Multi-Robot Systems

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Develop real-time tumor tracking algorithms for MRI-guided radiotherapy
• Evaluate tumor localization accuracy using multi-institutional MRI-linac data
• Improve motion management in radiotherapy with adaptive treatment strategies

Innovation

• Multi-institutional real-time MRI dataset
• Supports tumor tracking algorithm development
• Includes 0.35 T and 1.5 T MRI-linacs