We study the problem of registration for medical CT images from a novel perspective -- the sensitivity to degree of deformations in CT images. Although some learning-based methods have shown success in terms of average accuracy, their ability to handle regions with local large deformation (LLD) may significantly decrease compared to dealing with regions with minor deformation. This motivates our research into this issue. Two main causes of LLDs are organ motion and changes in tissue structure, with the latter often being a long-term process. In this paper, we propose a novel registration model called Cascade-Dilation Inter-Layer Differential Network (CDIDN), which exhibits both high deformation impedance capability (DIC) and accuracy. CDIDN improves its resilience to LLDs in CT images by enhancing LLDs in the displacement field (DF). It uses a feature-based progressive decomposition of LLDs, blending feature flows of different levels into a main flow in a top-down manner. It leverages Inter-Layer Differential Module (IDM) at each level to locally refine the main flow and globally smooth the feature flow, and also integrates feature velocity fields that can effectively handle feature deformations of various degrees. We assess CDIDN using lungs as representative organs with large deformation. Our findings show that IDM significantly enhances LLDs of the DF, by which improves the DIC and accuracy of the model. Compared with other outstanding learning-based methods, CDIDN exhibits the best DIC and excellent accuracy. Based on vessel enhancement and enhanced LLDs of the DF, we propose a novel method to accurately track the appearance, disappearance, enlargement, and shrinkage of pulmonary lesions, which effectively addresses detection of early lesions and peripheral lung lesions, issues of false enlargement, false shrinkage, and mutilation of lesions.

翻译：我们从新的视角——对CT图像形变程度的敏感性——研究医学CT图像的配准问题。尽管某些基于学习的方法在平均精度方面取得了成功，但相较于处理微小形变区域，它们处理局部大形变区域的能力可能显著下降，这促使我们对此问题展开研究。局部大形变的主要成因包括器官运动和组织结构变化，后者通常是长期过程。本文提出一种名为级联膨胀层间差分网络的新型配准模型，该模型兼具高形变阻抗能力和精度。CDIDN通过增强位移场中的局部大形变来提升对CT图像中局部大形变的鲁棒性。它采用基于特征的渐进分解策略处理局部大形变，以自上而下方式将不同层次的特征流融合至主流中；在各层级利用层间差分模块对主流进行局部细化并全局平滑特征流；同时集成能有效处理各程度特征形变的特征速度场。我们以肺部作为大形变代表器官评估CDIDN。结果表明，IDM显著增强了位移场的局部大形变，从而提升模型的形变阻抗能力和精度。与其他优秀基于学习方法相比，CDIDN展现出最优的形变阻抗能力和卓越的精度。基于血管增强和增强后的位移场局部大形变，我们提出一种新方法来精确追踪肺部病变的出现、消失、增大和缩小，有效解决了早期病变和周边肺病变的检测、假性增大、假性缩小及病变残缺问题。