Efficient Large-Deformation Medical Image Registration via Recurrent Dynamic Correlation

IEEE Transactions on Medical Imaging, vol. 45, pp. 1553–1565

Abstract

Deformable image registration estimates voxel-wise correspondences between images through spatial transformations, and plays a key role in medical imaging. While deep learning methods have significantly reduced runtime, efficiently handling large deformations remains a challenging task. Convolutional networks aggregate local features but lack direct modeling of voxel correspondences, promoting recent works to explore explicit feature matching. Among them, voxel-to-region matching is more efficient for direct correspondence modeling by computing local correlation features within neighbourhoods, while region-to-region matching incurs higher redundancy due to excessive correlation pairs across large regions. However, the inherent locality of voxel-to-region matching hinders the capture of long-range correspondences required for large deformations. To address this, we propose a Recurrent Correlation-based framework that dynamically relocates the matching region toward more promising positions. At each step, local matching is performed with low cost, and the estimated offset guides the next search region, supporting efficient convergence toward large deformations. In addition, we uses a lightweight recurrent update module with memory capacity and decouples motion-related and texture features to suppress semantic redundancy. We conduct extensive experiments on brain MRI and abdominal CT datasets under two settings: with and without affine pre-registration. Results show our method exhibits a strong accuracy-computation trade-off, surpassing or matching the state-of-the-art performance. For example, it achieves comparable performance on the non-affine OASIS dataset, while using only 9.5% of the FLOPs and running 96% faster than RDP, a representative high-performing method.

Authors 3

1. Tianran Li

   Shanghai Medical College of Fudan University · Fudan University

   Affiliation as printed

   Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

2. Marius Staring Aachen Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands

3. Yuchuan Qiao

   Shanghai Medical College of Fudan University · Fudan University

   Affiliation as printed

   Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China