将超分辨率嵌入重建流程，提升加速3D心脏MRI的细节还原能力。
Unrolled Reconstruction with Integrated Super-Resolution for Accelerated 3D LGE MRI
- 用超分辨率网络替代优化中的近端算子，实现分辨率增强与数据一致性的联合优化。
- 在不同加速倍数下，PSNR和SSIM均优于传统方法，左心房结构保留更完整。
- 适合需要高精度心脏影像的临床研究者，尤其关注心房细微结构的分析。
加速的3D延迟钆增强（LGE）MRI需要鲁棒的重建方法，以从欠采样k空间数据中恢复细薄的心房结构。尽管展开式模型驱动网络能有效结合物理驱动的数据一致性与学习先验，但其在原始采集分辨率下运行，难以充分恢复高频细节。我们提出一种混合展开式重建框架，用增强型深度超分辨率（EDSR）网络替换优化循环中每一步的近端算子，实现超分辨率增强与数据一致性强制的联合处理。该模型在回顾性欠采样的临床前3D LGE数据集上端到端训练，并与压缩感知、基于模型的深度学习（MoDL）及自引导深度图像先验（DIP）基线进行对比。在不同加速因子下，所提方法始终在PSNR和SSIM上优于标准展开式重建，更好地保留了精细心脏结构，提升了左心房（LA）分割性能。结果表明，将超分辨率先验直接整合至模型驱动重建中，可显著提升加速3D LGE MRI的成像质量。
原文摘要 · Abstract (English)
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.
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