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

Efe Ilıcak, Baris Imre, Chloé Najac, Ruben van den Broek, Beatrice Lena, Andrew Webb, Marius Staring

TL;DR

DUN-DD is introduced, 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 that outperforms state-of-the-art classical, data-driven, and physics-guided methods on both emulated and real portable MRI acquisitions.

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.

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

TL;DR

DUN-DD is introduced, 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 that outperforms state-of-the-art classical, data-driven, and physics-guided methods on both emulated and real portable MRI acquisitions.

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.
Paper Structure (11 sections, 3 equations, 2 figures, 3 tables)

This paper contains 11 sections, 3 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Problem Formulation
  4. Datasets
  5. Implementation Details
  6. Experiments and Results
  7. Ablation Study
  8. Emulated Dataset Results
  9. 47mT Portable MRI Results
  10. Discussion and Conclusion
  11. Acknowledgments

Figures (2)

  • Figure 1: Architecture of the proposed DUN-DD network. Each iteration includes parallel Fourier- and image-domain branches whose outputs are fused via an attention-based network, together with data consistency (DC) projections.
  • Figure 2: Representative reconstructions at $R=2$ for the 47mT portable MRI acquisitions, together with fully-sampled references. CS-TV exhibits excessive blurring, and U-Net shows residual aliasing artifacts. ISTA-Net reduces artifacts but the proposed DUN-DD achieves the better performance with superior artifact suppression and sharper details.