CRUNet-MR-Univ：一种用于多样化心脏MRI重建的通用模型
CRUNet-MR-Univ: A Foundation Model for Diverse Cardiac MRI Reconstruction
AI总结：
CRUNet-MR-Univ是一种基于时空相关性和提示先验知识的通用模型，用于提高心脏MRI重建在多样化场景下的泛化能力与性能表现。
摘要：
近年来，深度学习在心脏MRI（CMR）重建领域受到越来越多关注，因其在处理更高加速因子方面优于传统方法，展现出在实际临床应用中的潜力。然而，当前的深度学习方法在通用性方面仍存在局限。CMR扫描在图像对比度、采样模式、扫描仪厂商、解剖结构和疾病类型等方面表现出广泛差异。大多数现有模型仅针对单一或狭窄的这些变化进行设计，当面临分布偏移时会导致性能下降。因此，开发一个能够跨多样化CMR场景泛化的统一模型是有益的。为此，我们提出了CRUNet-MR-Univ，一种利用时空相关性和基于提示的先验知识的通用模型，以有效处理CMR扫描的全部多样性。我们的方法在各种设置中均优于基线方法，突显了其有效性和前景。
英文摘要：
In recent years, deep learning has attracted increasing attention in the field of Cardiac MRI (CMR) reconstruction due to its superior performance over traditional methods, particularly in handling higher acceleration factors, highlighting its potential for real-world clinical applications. However, current deep learning methods remain limited in generalizability. CMR scans exhibit wide variability in image contrast, sampling patterns, scanner vendors, anatomical structures, and disease types. Most existing models are designed to handle only a single or narrow subset of these variations, leading to performance degradation when faced with distribution shifts. Therefore, it is beneficial to develop a unified model capable of generalizing across diverse CMR scenarios. To this end, we propose CRUNet-MR-Univ, a foundation model that leverages spatio-temporal correlations and prompt-based priors to effectively handle the full diversity of CMR scans. Our approach consistently outperforms baseline methods across a wide range of settings, highlighting its effectiveness and promise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
