Sliding at first order: Higher-order momentum distributions for discontinuous image registration
Sliding at first order: Higher-order momentum distributions for discontinuous image registration
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DOI：
10.48550/arxiv.2303.07744
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Lili Bao;Jiahao Lu;Shihui Ying;S. Sommer
中科院分区：
文献类型：
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作者：
Lili Bao;Jiahao Lu;Shihui Ying;S. Sommer
在本文中，我们提出了一种新的方法来变形图像配准，捕捉滑动运动。大变形几何度量映射（LDDMM）配准方法在表示滑动运动时面临挑战，因为它每次构造都会产生平滑的翘曲。为了解决这个问题，我们将零阶和一阶动量与不可微内核扩展LDDMM。这允许表示在切换边界处的不连续变形和均匀区域中的非均匀变形。我们提供了一个数学分析的变形模型从不连续系统的观点。为了评估我们的方法，我们对人工图像和公开的DIR-Lab 4DCT数据集进行了实验。结果表明，我们的方法在捕捉合理的滑动运动的有效性。
In this paper, we propose a new approach to deformable image registration that captures sliding motions. The large deformation diffeomorphic metric mapping (LDDMM) registration method faces challenges in representing sliding motion since it per construction generates smooth warps. To address this issue, we extend LDDMM by incorporating both zeroth- and first-order momenta with a non-differentiable kernel. This allows to represent both discontinuous deformation at switching boundaries and diffeomorphic deformation in homogeneous regions. We provide a mathematical analysis of the proposed deformation model from the viewpoint of discontinuous systems. To evaluate our approach, we conduct experiments on both artificial images and the publicly available DIR-Lab 4DCT dataset. Results show the effectiveness of our approach in capturing plausible sliding motion.