Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

UEPS: Robust and Efficient MRI Reconstruction

Xiang Zhou, Hong Shang, Zijian Zhan, Tianyu He, Jintao Meng, Dong Liang

Abstract

Deep unrolled models (DUMs) have become the state of the art for accelerated MRI reconstruction, yet their robustness under domain shift remains a critical barrier to clinical adoption. In this work, we identify coil sensitivity map (CSM) estimation as the primary bottleneck limiting generalization. To address this, we propose UEPS, a novel DUM architecture featuring three key innovations: (i) an Unrolled Expanded (UE) design that eliminates CSM dependency by reconstructing each coil independently; (ii) progressive resolution, which leverages k-space-to-image mapping for efficient coarse-to-fine refinement; and (iii) sparse attention tailored to MRI's 1D undersampling nature. These physics-grounded designs enable simultaneous gains in robustness and computational efficiency. We construct a large-scale zero-shot transfer benchmark comprising 10 out-of-distribution test sets spanning diverse clinical shifts -- anatomy, view, contrast, vendor, field strength, and coil configurations. Extensive experiments demonstrate that UEPS consistently and substantially outperforms existing DUM, end-to-end, diffusion, and untrained methods across all OOD tests, achieving state-of-the-art robustness with low-latency inference suitable for real-time deployment.

UEPS: Robust and Efficient MRI Reconstruction

Abstract

Deep unrolled models (DUMs) have become the state of the art for accelerated MRI reconstruction, yet their robustness under domain shift remains a critical barrier to clinical adoption. In this work, we identify coil sensitivity map (CSM) estimation as the primary bottleneck limiting generalization. To address this, we propose UEPS, a novel DUM architecture featuring three key innovations: (i) an Unrolled Expanded (UE) design that eliminates CSM dependency by reconstructing each coil independently; (ii) progressive resolution, which leverages k-space-to-image mapping for efficient coarse-to-fine refinement; and (iii) sparse attention tailored to MRI's 1D undersampling nature. These physics-grounded designs enable simultaneous gains in robustness and computational efficiency. We construct a large-scale zero-shot transfer benchmark comprising 10 out-of-distribution test sets spanning diverse clinical shifts -- anatomy, view, contrast, vendor, field strength, and coil configurations. Extensive experiments demonstrate that UEPS consistently and substantially outperforms existing DUM, end-to-end, diffusion, and untrained methods across all OOD tests, achieving state-of-the-art robustness with low-latency inference suitable for real-time deployment.
Paper Structure (26 sections, 8 equations, 8 figures, 4 tables)

This paper contains 26 sections, 8 equations, 8 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Deep Unrolled Models
  4. Multi-Resolution
  5. Sparse Attention
  6. Proposed Method
  7. Problem Formulation
  8. Unrolled Expended Model
  9. Progressive Resolution
  10. Sparse Attention
  11. Experiments and Results
  12. Experimental Setups
  13. Implementation Details
  14. Zero-shot Transfer Benchmark Results
  15. Ablation Studies
  16. ...and 11 more sections

Figures (8)

  • Figure 1: Zero-shot transfer benchmark results. Average PSNR: mean over 10 OOD sets. Runtime: measured on FastMRI Knee (15 coils).
  • Figure 2: Overview of the proposed UEPS architecture
  • Figure 3: Examples of CSM estimation
  • Figure 4: Exmaples of attention scores
  • Figure 5: Reconstruction examples from the zero-shot transfer benchmark, each with PSNR/SSIM shown on the top left corner.
  • ...and 3 more figures