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DDMamba: A Dual-Domain Mamba for Multi-Modal Magnetic Resonance Imaging Reconstruction With Fourier Fusion.

Zhijin Lin¹, Jie Yang², Teng Yu¹

¹College of Electronics and Information, Qingdao University, Qingdao, China.

Magnetic Resonance in Medicine

|November 2, 2025

Summary

A new Dual-domain Mamba Network (DDMamba) enhances multi-modal MRI reconstruction by integrating spatial and wavelet features. This method offers more efficient and reliable magnetic resonance imaging (MRI) solutions.

Keywords:

Magnetic Resonance Imaging reconstruction Mamba feature fusion frequency analysis multi‐modal

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Area of Science:

Medical Imaging
Artificial Intelligence
Signal Processing

Background:

Multi-modal magnetic resonance (MR) imaging leverages shared information for enhanced reconstruction.
Existing methods require improvement for efficiency and reliability in MRI reconstruction.

Purpose of the Study:

To introduce a novel Dual-domain Mamba Network (DDMamba) for efficient and reliable multi-modal MRI reconstruction.
To address the challenge of reconstructing significantly under-sampled MR images.

Main Methods:

The proposed DDMamba utilizes Spatial Mamba for multi-scale feature extraction and Wavelet Mamba with a Wavelet 2D-Selective-Scan strategy.
Fourier fusion integrates spatial, wavelet, and global frequency domain information.
The network refines spatial details and establishes low-to-high frequency dependencies.

Main Results:

DDMamba demonstrated superior performance on public datasets (NAMIC, BraTS, fastMRI).
Quantitative metrics and visual assessments showed significant improvements over state-of-the-art methods.
The method effectively reconstructs multi-modal single-coil MRI data.

Conclusions:

The Dual-domain Mamba network optimizes multi-feature representation through Fourier fusion of spatial and wavelet domains.
This approach significantly enhances multi-modal single-coil MRI reconstruction.
DDMamba provides efficient and reliable imaging solutions for clinical applications.