Physics-Guided Dual-Domain Network with Attention-Based Fusion for Portable MRI Reconstruction

TL;DR

This paper introduces DUN-DD, a physics-guided dual-domain neural network with attention fusion, significantly improving portable MRI reconstruction quality by leveraging both k-space and image-domain data.

Contribution

The paper presents a novel 3D dual-domain network with attention-based fusion specifically designed for portable MRI reconstruction, integrating physics-guided modeling and dual-domain processing.

Findings

DUN-DD outperforms existing methods on emulated datasets.

DUN-DD achieves superior results on real portable MRI data.

The approach enhances image quality in low-field portable MRI systems.

Abstract

Portable low-field magnetic resonance imaging (MRI) systems have gained renewed interest owing to their cost effectiveness and point-of-care imaging capabilities. Yet, portable MRI systems suffer from relatively low signal-to-noise ratio and limited hardware capabilities. While previous works have proposed the use of deep learning based reconstruction methods to improve low-field image quality, these operated only in the image-domain. Unlike other imaging modalities, MRI directly acquires data in the Fourier-domain (k-space), and exploiting both k-space and image-domain information can improve reconstruction quality. Here, we introduce DUN-DD, a novel physics-guided 3D network for portable MRI reconstruction, with parallel dual-domain branches whose outputs are combined together via an attention-based fusion network. To demonstrate the performance of the proposed method, we present \textit{in vivo} reconstructions obtained from both emulated datasets as well as images acquired with a 47mT Halbach-based portable MRI system. Our results show that DUN-DD outperforms state-of-the-art classical, data-driven, and physics-guided methods on both emulated and real portable MRI acquisitions.