Unrolled Reconstruction with Integrated Super-Resolution for Accelerated 3D LGE MRI
This work addresses a domain-specific problem in medical imaging for cardiac MRI, offering incremental improvements over existing methods.
The paper tackled the problem of recovering thin atrial structures from undersampled k-space data in accelerated 3D LGE MRI by proposing a hybrid unrolled reconstruction framework that integrates super-resolution enhancement, resulting in improved PSNR, SSIM, and better preservation of fine cardiac structures across acceleration factors.
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.