Image Content Matters: An Image Content Aware State Space Model for Accelerated MRI Reconstruction

The challenge of accelerated MRI reconstruction lies in recovering high-quality images from undersampled k-space. Recently, the selective state space model (Mamba) has shown promising results in various tasks with balanced global receptive field and computational efficiency, shedding new light on MRI reconstruction. However, existing approaches directly flatten 2D images based on spatial positions and apply Mamba to vision tasks, failing to preserve and explore the content properties. In this paper, we posit that the key to unlocking Mamba's full potential for MRI reconstruction lies in content-aware sequence modeling. We investigate two fundamental challenges: (1) how to reasonably preserve semantic information when converting 2D images into 1D sequences, and (2) how to effectively identify and recover the crucial high-frequency textures. To this end, we introduce CAM, a novel framework that shifts Mamba-based MRI reconstruction from position-based to content-aware sequence modeling. Specifically, we introduce three modules: (1) the Semantic Preservation Scanning Module (SPSM) introduces learnable clustering centers to group similar pixels, establishing the semantic preserved sequence. (2) The Texture Extraction Scanning Module (TESM) acts as a differentiable local texture descriptor to estimate crucial high-frequency information, forming the texture emphasized sequence. (3) The Texture Enhancement Mamba Module (TEMM) further modulates the semantic sequence with texture-informed system matrices derived from the texture sequence, yielding both context- and texture-aware sequential representations. With these enhancements, CAM significantly outperforms existing methods across various datasets and under-sampling masks.

Authors

• Yucong Meng Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai 200032, China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai 200032, China
• Zhiwei Yang Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai 200032, China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai 200032, China Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
• Kexue Fu Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science
• Zhijian Song Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai 200032, China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai 200032, China
• Yonghong Shi Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai 200032, China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai 200032, China

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