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

TL;DR

This paper introduces a hybrid unrolled reconstruction method for accelerated 3D LGE MRI that integrates super-resolution within the iterative process, improving image quality and structural preservation.

Contribution

A novel framework replacing the proximal operator with a super-resolution network within unrolled MRI reconstruction, enhancing detail recovery and segmentation accuracy.

Findings

Consistently improves PSNR and SSIM over baseline methods.

Better preserves fine cardiac structures in reconstructed images.

Enhances LA segmentation performance.

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.