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 sensi...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/9/5166 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2076-3417 |