Medical Image Registration via Spatial Feature Extraction Mamba and Substrate Iterative Refinement

One of the major challenges in medical image registration is balancing computational efficiency with the ability to capture large deformations in complex anatomical structures. Existing methods often struggle with high computational costs due to the need for extensive feature extraction and attention computations at various levels of the network. Moreover, some methods do not take into account the spatial relationships of the feature images during registration, and the loss of these spatial relationships leads to suboptimal results for these methods. To this end, we introduce a novel medical image registration network, PSMamba-Net, which leverages optimized iteration and the Mamba framework within a dual-stream pyramid architecture. The network reduces the computational burden by narrowing attention computations at each decoding level, while an optimized iterative registration module at the bottom of the pyramid captures large deformations. This approach eliminates the need for repeated feature extraction, significantly accelerating the registration process. Additionally, the SMB module is incorporated as a decoder to enhance spatial relationship modelling and leverage Mamba's strengths in long-sequence processing. PSMamba-Net balances efficiency and accuracy, surpassing state-of-the-art methods across LPBA40, Mindboggle, and Abdomen CT datasets. Our source code is available at: https://github.com/VCMHE/PSMamba.