Magnetic resonance imaging (MRI) is a widely used radiological modality renowned for its radiation-free, comprehensive insights into the human body, facilitating medical diagnoses. However, the drawback of prolonged scan times hinders its accessibility. The k-space undersampling offers a solution, yet the resultant artifacts necessitate meticulous removal during image reconstruction. Although Deep Learning (DL) has proven effective for fast MRI image reconstruction, its broader applicability across various imaging scenarios has been constrained. Challenges include the high cost and privacy restrictions associated with acquiring large-scale, diverse training data, coupled with the inherent difficulty of addressing mismatches between training and target data in existing DL methodologies. Here, we present a novel Physics-Informed Synthetic data learning framework for Fast MRI, called PISF. PISF marks a breakthrough by enabling generalized DL for multi-scenario MRI reconstruction through a single trained model. Our approach separates the reconstruction of a 2D image into many 1D basic problems, commencing with 1D data synthesis to facilitate generalization. We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%. Additionally, PISF exhibits remarkable generalizability across multiple vendors and imaging centers. Its adaptability to diverse patient populations has been validated through evaluations by ten experienced medical professionals. PISF presents a feasible and cost-effective way to significantly boost the widespread adoption of DL in various fast MRI applications.

翻译：磁共振成像（MRI）是一种广泛使用的放射学模态，以无辐射、全面洞察人体结构而闻名，有助于医学诊断。然而，扫描时间较长这一缺点限制了其可及性。k空间欠采样提供了一种解决方案，但由此产生的伪影在图像重建过程中需要精心去除。尽管深度学习（DL）已被证明在快速MRI图像重建中有效，但其在各种成像场景中的广泛适用性仍受到限制。挑战包括获取大规模、多样化的训练数据所需的高成本和隐私限制，以及现有DL方法中难以处理训练数据与目标数据之间不匹配的固有问题。本文提出了一种新颖的物理信息合成数据学习框架PISF，用于快速MRI。PISF通过单一训练模型实现了多场景MRI重建的泛化DL，取得突破性进展。我们的方法将二维图像重建分解为多个一维基本问题，从一维数据合成入手以促进泛化。我们证明，在合成数据上训练DL模型并辅以增强学习技术，能够产生与在匹配的真实数据集上训练的模型相当甚至更优的体内MRI重建结果，从而将对真实MRI数据的依赖降低多达96%。此外，PISF在多个供应商和成像中心之间表现出显著的泛化能力。其对不同患者群体的适应性已通过十位经验丰富的医学专业人员的评估得到验证。PISF提供了一种可行且成本效益高的方式，显著推动了DL在各种快速MRI应用中的广泛采用。