Robust Multi-Scale Implicit Neural Representations for Large-Deformation Lung Registration

Abstract

We propose a multi-scale Implicit Neural Representation (INR) framework for dense deformable image registration, designed to stabilize convergence for large deformations while preserving precision for fine anatomical details. We model the INR as a dual-branch architecture that explicitly decomposes the motion into global and local components. The objective function is driven by mask-guided Normalized Cross-Correlation augmented by geometric and semantic regularization to ensure smooth, anatomically plausible motion. Evaluation on the DIR-Lab 4DCT thorax dataset demonstrates competitive performance with a mean Target Registration Error (TRE) below 1.0\,mm. On the more challenging DIR-Lab COPDgene thorax dataset, the model achieves robust alignment with a mean TRE of 1.23\,mm, yielding performance comparable to leading classical optimization frameworks. A comprehensive ablation study confirms that the dual-branch design and multi-scale optimization strategy are necessary to achieve these results, enabling stable registration with modest computational overhead.