标题： GENRE-CMR：多样化多领域心脏MRI重建的可推广深度学习 显示英文标题

标题： GENRE-CMR: Generalizable Deep Learning for Diverse Multi-Domain Cardiac MRI Reconstruction

摘要： 加速心血管磁共振（CMR）图像重建由于扫描时间和图像质量之间的权衡，仍然是一个关键挑战，尤其是在跨不同采集设置进行泛化时。 我们提出GENRE-CMR，一种基于生成对抗网络（GAN）的架构，采用残差深度展开重建框架以提高重建保真度和泛化能力。 该架构将迭代优化展开为一系列卷积子网络，通过残差连接丰富特征传播，从浅层到深层阶段逐步传递特征。 为进一步提升性能，我们集成了两个损失函数： (1) 边缘感知区域（EAR）损失，引导网络关注结构信息丰富的区域，并有助于防止常见的重建模糊；以及 (2) 统计分布对齐（SDA）损失，通过对称KL散度公式在不同数据分布之间正则化特征空间。 大量实验确认GENRE-CMR在训练数据和未见数据上均优于最先进方法，在各种加速因子和采样轨迹的未见分布上实现了0.9552 SSIM和38.90 dB PSNR。 消融研究确认了每个提出的组件对重建质量和泛化能力的贡献。 我们的框架为高质量CMR重建提供了一个统一且稳健的解决方案，为在异构采集协议中实现临床适应性部署铺平了道路。 显示英文摘要

摘要： Accelerated Cardiovascular Magnetic Resonance (CMR) image reconstruction remains a critical challenge due to the trade-off between scan time and image quality, particularly when generalizing across diverse acquisition settings. We propose GENRE-CMR, a generative adversarial network (GAN)-based architecture employing a residual deep unrolled reconstruction framework to enhance reconstruction fidelity and generalization. The architecture unrolls iterative optimization into a cascade of convolutional subnetworks, enriched with residual connections to enable progressive feature propagation from shallow to deeper stages. To further improve performance, we integrate two loss functions: (1) an Edge-Aware Region (EAR) loss, which guides the network to focus on structurally informative regions and helps prevent common reconstruction blurriness; and (2) a Statistical Distribution Alignment (SDA) loss, which regularizes the feature space across diverse data distributions via a symmetric KL divergence formulation. Extensive experiments confirm that GENRE-CMR surpasses state-of-the-art methods on training and unseen data, achieving 0.9552 SSIM and 38.90 dB PSNR on unseen distributions across various acceleration factors and sampling trajectories. Ablation studies confirm the contribution of each proposed component to reconstruction quality and generalization. Our framework presents a unified and robust solution for high-quality CMR reconstruction, paving the way for clinically adaptable deployment across heterogeneous acquisition protocols.