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CT图像形变程度的敏感性这一新视角出发，研究医学CT图像配准问题。尽管基于学习的方法在平均精度上已取得一定成功，但其处理局部大形变区域的能力相较于微小形变区域可能显著下降，这促使我们对该问题展开研究。局部大形变主要由器官运动和组织结构变化两种因素引起，其中后者通常为长期演变过程。本文提出一种名为级联扩张层间差分网络(Cascade-Dilation Inter-Layer Differential Network, CDIDN)的新型配准模型，该模型兼具高形变阻抗能力与精度。CDIDN通过增强位移场中的局部大形变来提升其对CT图像形变的鲁棒性：采用基于特征的分层分解策略，将不同层级的特征流自上而下融合至主特征流，并利用各层级的层间差分模块(IDM)对主特征流进行局部细化与全局平滑，同时集成可有效处理多尺度形变的特征速度场。我们以肺作为大形变代表性器官进行评估，结果表明IDM能显著增强位移场的局部大形变，从而提升模型的形变阻抗能力与精度。相较于其他优秀深度学习方法，CDIDN展现出最优的形变阻抗能力与卓越的配准精度。基于血管增强技术与强化后的位移场局部大形变，我们提出一种可精准追踪肺病灶出现、消失、扩大与缩小的新方法，有效解决了早期病灶、肺外周病灶检测以及病灶伪扩大、伪缩小与残缺识别等难题。