DINOMotion: advanced robust tissue motion tracking with DINOv2 in 2D-Cine MRI-guided radiotherapy

-- Accurate tissue motion tracking is critical to ensure treatment outcome and safety in 2D-Cine MRIguided radiotherapy. This is typically achieved by registration of sequential images, but existing methods often face challenges with large misalignments and lack of in-terpretability. In this paper, we introduce DINOMotion, a novel deep learning framework based on DINOv2 with Low-Rank Adaptation (LoRA) layers for robust, efficient, and interpretable motion tracking. DINOMotion automatically detects corresponding landmarks to derive optimal image registration, enhancing interpretability by providing explicit visual correspondences between sequential images. The integration of LoRA layers reduces trainable parameters, improving training efficiency, while DINOv2's powerful feature representations offer robustness against large misalignments. Unlike iterative optimization-based methods, DINOMotion directly computes image registration at test time. Our experiments on volunteer and patient datasets demonstrate its effectiveness in estimating both linear and nonlinear transformations, achieving Dice scores of 92.07% DINOMotion processes each scan in approximately 30ms and consistently outperforms state-of-the-art methods, particularly in handling large misalignments. Soorena Salari and Yiming Xiao are with the Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada (e-mail: soorena.salari@concordia.ca and yim-ing.xiao@concordia.ca). Hassan Rivaz is with the Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada (e-mail: has-san.rivaz@concordia.ca). Catherine Spino, Laurie-Anne Pharand, and Fabienne Lath-uiliere were with the Elekta Ltd., Montreal, Canada (e-mail: catherine.spino@polymtl.ca,