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Unrolled Reconstruction with Integrated Super-Resolution for Accelerated 3D LGE MRI

Md Hasibul Husain Hisham, Shireen Elhabian, Ganesh Adluru, Jason Mendes, Andrew Arai, Eugene Kholmovski, Ravi Ranjan, Edward DiBella

Abstract

Accelerated 3D late gadolinium enhancement (LGE) MRI requires robust reconstruction methods to recover thin atrial structures from undersampled k-space data. While unrolled model-based networks effectively integrate physics-driven data consistency with learned priors, they operate at the acquired resolution and may fail to fully recover high-frequency detail. We propose a hybrid unrolled reconstruction framework in which an Enhanced Deep Super-Resolution (EDSR) network replaces the proximal operator within each iteration of the optimization loop, enabling joint super-resolution enhancement and data consistency enforcement. The model is trained end-to-end on retrospectively undersampled preclinical 3D LGE datasets and compared against compressed sensing, Model-Based Deep Learning (MoDL), and self-guided Deep Image Prior (DIP) baselines. Across acceleration factors, the proposed method consistently improves PSNR and SSIM over standard unrolled reconstruction and better preserves fine cardiac structures, leading to improved LA (left atrium) segmentation performance. These results demonstrate that integrating super-resolution priors directly within model-based reconstruction provides measurable gains in accelerated 3D LGE MRI.

Unrolled Reconstruction with Integrated Super-Resolution for Accelerated 3D LGE MRI

Abstract

Accelerated 3D late gadolinium enhancement (LGE) MRI requires robust reconstruction methods to recover thin atrial structures from undersampled k-space data. While unrolled model-based networks effectively integrate physics-driven data consistency with learned priors, they operate at the acquired resolution and may fail to fully recover high-frequency detail. We propose a hybrid unrolled reconstruction framework in which an Enhanced Deep Super-Resolution (EDSR) network replaces the proximal operator within each iteration of the optimization loop, enabling joint super-resolution enhancement and data consistency enforcement. The model is trained end-to-end on retrospectively undersampled preclinical 3D LGE datasets and compared against compressed sensing, Model-Based Deep Learning (MoDL), and self-guided Deep Image Prior (DIP) baselines. Across acceleration factors, the proposed method consistently improves PSNR and SSIM over standard unrolled reconstruction and better preserves fine cardiac structures, leading to improved LA (left atrium) segmentation performance. These results demonstrate that integrating super-resolution priors directly within model-based reconstruction provides measurable gains in accelerated 3D LGE MRI.
Paper Structure (14 sections, 9 equations, 2 figures, 3 tables)

This paper contains 14 sections, 9 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Baselines
  4. Proposed Method: Unrolled Reconstruction with EDSR
  5. Vanilla Unrolled Variant.
  6. Results
  7. Implementation Details
  8. Dataset
  9. Training
  10. Analysis
  11. Baselines
  12. Downstream Segmentation
  13. Ablation Experiments.
  14. Conclusion

Figures (2)

  • Figure 1: Proposed Unrolled EDSR framework. The EDSR Net serves as the learned proximal operator at each of the $N$ unrolled iterations, with weights $\phi$ shared across iterations. The initial reconstruction $\mathbf{x}^{(0)} = \mathbf{A}^H\mathbf{y}$ is the zero-filled adjoint, and data consistency (DC) enforces k-space fidelity via conjugate gradient at each step.
  • Figure 2: Qualitative comparison of reconstruction methods at acceleration factors $R=4$ and $R=6$. Each column shows a representative axial slice from a preclinical subject. At $R=4$, Unrolled+EDSR and Unrolled both recover fine cardiac structures faithfully, while Compressed Sensing introduces visible artifacts and DIP shows smoothing. At $R=6$, quality degrades across all methods, with Unrolled+EDSR best preserving structural detail. The LA is zoomed in for better visibility.