标题： DINOMotion：在2D-Cine MRI引导放疗中使用DINOv2的先进鲁棒组织运动跟踪 显示英文标题

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

摘要： 准确的组织运动跟踪对于确保二维动态MRI引导放疗的治疗效果和安全性至关重要。 这通常是通过序列图像的配准来实现的，但现有方法在处理大范围错位和缺乏可解释性方面常常面临挑战。 在本文中，我们介绍了DINOMotion，这是一种基于DINOv2的新型深度学习框架，结合了低秩适应（LoRA）层，用于鲁棒、高效且可解释的运动跟踪。 DINOMotion自动检测对应地标以推导最佳图像配准，通过提供序列图像之间的显式视觉对应关系来增强可解释性。 LoRA层的集成减少了可训练参数，提高了训练效率，而DINOv2的强大特征表示则增强了对大范围错位的鲁棒性。 与基于迭代优化的方法不同，DINOMotion在测试时直接计算图像配准。 我们在志愿者和患者数据集上的实验表明，它在估计线性和非线性变换方面都有效，肾脏的Dice分数达到92.07%，肝脏为90.90%，肺部为95.23%，相应的Hausdorff距离分别为5.47毫米、8.31毫米和6.72毫米。 DINOMotion每个扫描处理时间约为30毫秒，并且始终优于最先进方法，尤其是在处理大范围错位方面表现突出。 这些结果突显了其作为实时运动跟踪在二维动态MRI引导放疗中的鲁棒和可解释解决方案的潜力。 显示英文摘要

摘要： Accurate tissue motion tracking is critical to ensure treatment outcome and safety in 2D-Cine MRI-guided radiotherapy. This is typically achieved by registration of sequential images, but existing methods often face challenges with large misalignments and lack of interpretability. 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% for the kidney, 90.90% for the liver, and 95.23% for the lung, with corresponding Hausdorff distances of 5.47 mm, 8.31 mm, and 6.72 mm, respectively. DINOMotion processes each scan in approximately 30ms and consistently outperforms state-of-the-art methods, particularly in handling large misalignments. These results highlight its potential as a robust and interpretable solution for real-time motion tracking in 2D-Cine MRI-guided radiotherapy.