Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration

Deformable lung CT registration plays a crucial role in clinical applications such as respiratory motion tracking, disease progression analysis, and radiotherapy planning. While voxel-based registration has traditionally dominated this domain, it often suffers from high computational costs and sensitivity to intensity variations. In this work, we propose a novel point-based deformable registration framework tailored to the unique challenges of lung CT alignment. Our approach combines geometric keypoint attention at coarse resolutions to enhance the global correspondence with attention-based refinement modules at finer scales to accurately model subtle anatomical deformations. Furthermore, we adopt a bi-directional training strategy that enforces forward and backward consistency through cycle supervision, promoting anatomically coherent transformations. We evaluate our method on the large-scale Lung250M benchmark and achieve state-of-the-art results, significantly surpassing the existing voxel-based and point-based baselines in the target registration accuracy. These findings highlight the potential of sparse geometric modeling for complex respiratory motion and establish a strong foundation for future point-based deformable registration in thoracic imaging.