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We study the problem of registration for medical CT images from a novelperspective -- the sensitivity to degree of deformations in CT images. Althoughsome learning-based methods have shown success in terms of average accuracy,their ability to handle regions with local large deformation (LLD) maysignificantly decrease compared to dealing with regions with minor deformation.This motivates our research into this issue. Two main causes of LLDs are organmotion and changes in tissue structure, with the latter often being a long-termprocess. In this paper, we propose a novel registration model calledCascade-Dilation Inter-Layer Differential Network (CDIDN), which exhibits bothhigh deformation impedance capability (DIC) and accuracy. CDIDN improves itsresilience to LLDs in CT images by enhancing LLDs in the displacement field(DF). It uses a feature-based progressive decomposition of LLDs, blendingfeature flows of different levels into a main flow in a top-down manner. Itleverages Inter-Layer Differential Module (IDM) at each level to locally refinethe main flow and globally smooth the feature flow, and also integrates featurevelocity fields that can effectively handle feature deformations of variousdegrees. We assess CDIDN using lungs as representative organs with largedeformation. Our findings show that IDM significantly enhances LLDs of the DF,by which improves the DIC and accuracy of the model. Compared with otheroutstanding learning-based methods, CDIDN exhibits the best DIC and excellentaccuracy. Based on vessel enhancement and enhanced LLDs of the DF, we propose anovel method to accurately track the appearance, disappearance, enlargement,and shrinkage of pulmonary lesions, which effectively addresses detection ofearly lesions and peripheral lung lesions, issues of false enlargement, falseshrinkage, and mutilation of lesions.

CDIDN: A Registration Model with High Deformation Impedance Capability for Long-Term Tracking of Pulmonary Lesion Dynamics