Magnetic resonance imaging (MRI) is a principal radiological modality that provides radiation-free, abundant, and diverse information about the whole human body for medical diagnosis, but suffers from prolonged scan time. The scan time can be significantly reduced through k-space undersampling but the introduced artifacts need to be removed in image reconstruction. Although deep learning (DL) has emerged as a powerful tool for image reconstruction in fast MRI, its potential in multiple imaging scenarios remains largely untapped. This is because not only collecting large-scale and diverse realistic training data is generally costly and privacy-restricted, but also existing DL methods are hard to handle the practically inevitable mismatch between training and target data. Here, we present a Physics-Informed Synthetic data learning framework for Fast MRI, called PISF, which is the first to enable generalizable DL for multi-scenario MRI reconstruction using solely one trained model. For a 2D image, the reconstruction is separated into many 1D basic problems and starts with the 1D data synthesis, to facilitate generalization. We demonstrate that training DL models on synthetic data, integrated with enhanced learning techniques, can achieve comparable or even better in vivo MRI reconstruction compared to models trained on a matched realistic dataset, reducing the demand for real-world MRI data by up to 96%. Moreover, our PISF shows impressive generalizability in multi-vendor multi-center imaging. Its excellent adaptability to patients has been verified through 10 experienced doctors' evaluations. PISF provides a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications, while freeing from the intractable ethical and practical considerations of in vivo human data acquisitions.

翻译：磁共振成像（MRI）是一种主要的放射学模态，能为医学诊断提供无辐射、丰富且多样的人体全身信息，但存在扫描时间过长的问题。通过k空间欠采样可显著缩短扫描时间，但引入的伪影需在图像重建中去除。尽管深度学习（DL）已成为快速MRI图像重建的有力工具，其在多成像场景中的潜力尚未充分挖掘。这不仅是因为收集大规模、多样化的真实训练数据通常成本高昂且受隐私限制，还因为现有DL方法难以处理训练数据与目标数据之间实际不可避免的失配问题。本文提出首个物理信息合成数据学习框架PISF（Physics-Informed Synthetic data learning framework for Fast MRI），该方法仅使用单一训练模型即可实现多场景MRI重建的泛化DL。对于二维图像，重建被分解为多个一维基本问题，并从一维数据合成开始以促进泛化。我们证明，在合成数据上训练DL模型并整合增强学习技术，可实现与在匹配真实数据集上训练的模型相当甚至更优的活体MRI重建，将真实MRI数据需求量降低高达96%。此外，我们的PISF在多供应商多中心成像中展现出令人印象深刻的泛化能力。通过10位经验丰富的医生的评估，验证了其对患者的优异适应性。PISF提供了一种可行且经济高效的方式，显著推动DL在各类快速MRI应用中的广泛使用，同时摆脱了活体人体数据采集中棘手的伦理和实际问题。