  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.
