PAS-Mamba: Phase-Amplitude-Spatial State Space Model for MRI Reconstruction

TL;DR

PAS-Mamba introduces a novel MRI reconstruction framework that decouples phase and amplitude modeling in the frequency domain and fuses this with image features, significantly improving reconstruction quality.

Contribution

It proposes a new decoupled phase-amplitude modeling approach with specialized frequency scanning and a fusion module, advancing MRI reconstruction techniques.

Findings

Outperforms state-of-the-art methods on IXI and fastMRI datasets.

Effectively disentangles phase and amplitude for better feature representation.

Enhances image reconstruction quality with bidirectional frequency-image domain fusion.

Abstract

Joint feature modeling in both the spatial and frequency domains has become a mainstream approach in MRI reconstruction. However, existing methods generally treat the frequency domain as a whole, neglecting the differences in the information carried by its internal components. According to Fourier transform theory, phase and amplitude represent different types of information in the image. Our spectrum swapping experiments show that magnitude mainly reflects pixel-level intensity, while phase predominantly governs image structure. To prevent interference between phase and magnitude feature learning caused by unified frequency-domain modeling, we propose the Phase-Amplitude-Spatial State Space Model (PAS-Mamba) for MRI Reconstruction, a framework that decouples phase and magnitude modeling in the frequency domain and combines it with image-domain features for better reconstruction. In the image domain, LocalMamba preserves spatial locality to sharpen fine anatomical details. In frequency domain, we disentangle amplitude and phase into two specialized branches to avoid representational coupling. To respect the concentric geometry of frequency information, we propose Circular Frequency Domain Scanning (CFDS) to serialize features from low to high frequencies. Finally, a Dual-Domain Complementary Fusion Module (DDCFM) adaptively fuses amplitude phase representations and enables bidirectional exchange between frequency and image domains, delivering superior reconstruction. Extensive experiments on the IXI and fastMRI knee datasets show that PAS-Mamba consistently outperforms state of the art reconstruction methods.