Deform-Mamba Network for MRI Super-Resolution
This work addresses MRI super-resolution for medical imaging, but it appears incremental as it builds on existing methods with hybrid components.
The authors tackled MRI super-resolution by proposing the Deform-Mamba network, which combines deformable blocks and vision Mamba blocks to explore local and global image information, achieving competitive performance on IXI and fastMRI datasets.
In this paper, we propose a new architecture, called Deform-Mamba, for MR image super-resolution. Unlike conventional CNN or Transformer-based super-resolution approaches which encounter challenges related to the local respective field or heavy computational cost, our approach aims to effectively explore the local and global information of images. Specifically, we develop a Deform-Mamba encoder which is composed of two branches, modulated deform block and vision Mamba block. We also design a multi-view context module in the bottleneck layer to explore the multi-view contextual content. Thanks to the extracted features of the encoder, which include content-adaptive local and efficient global information, the vision Mamba decoder finally generates high-quality MR images. Moreover, we introduce a contrastive edge loss to promote the reconstruction of edge and contrast related content. Quantitative and qualitative experimental results indicate that our approach on IXI and fastMRI datasets achieves competitive performance.