MMR-Mamba: Multi-Modal MRI Reconstruction with Mamba and Spatial-Frequency Information Fusion
Jing Zou, Lanqing Liu, Qi Chen, Shujun Wang, Zhanli Hu, Xiaohan Xing,, and Jing Qin

TL;DR
This paper introduces MMR-Mamba, a novel multi-modal MRI reconstruction framework that efficiently combines spatial and frequency domain information using Mamba's long-range dependency modeling, significantly improving image quality from under-sampled data.
Contribution
The paper proposes a new multi-modal MRI reconstruction method integrating spatial and frequency domain features with linear complexity, addressing limitations of existing convolutional and transformer models.
Findings
Effective reconstruction of target modality images from under-sampled data.
Superior performance over existing methods in quality and efficiency.
Enhanced integration of multi-modal information through novel modules.
Abstract
Multi-modal MRI offers valuable complementary information for diagnosis and treatment; however, its utility is limited by prolonged scanning times. To accelerate the acquisition process, a practical approach is to reconstruct images of the target modality, which requires longer scanning times, from under-sampled k-space data using the fully-sampled reference modality with shorter scanning times as guidance. The primary challenge of this task is comprehensively and efficiently integrating complementary information from different modalities to achieve high-quality reconstruction. Existing methods struggle with this: 1) convolution-based models fail to capture long-range dependencies; 2) transformer-based models, while excelling in global feature modeling, struggle with quadratic computational complexity. To address this, we propose MMR-Mamba, a novel framework that thoroughly and…
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Taxonomy
TopicsAdvanced MRI Techniques and Applications · Medical Imaging Techniques and Applications · Advanced Neuroimaging Techniques and Applications
