Fast Magnetic Resonance Imaging

Fast magnetic resonance imaging (MRI) aims to significantly reduce scan times while maintaining high image quality, addressing a major limitation of conventional MRI. Current research heavily utilizes deep learning, employing various architectures such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformers, often incorporating physics-informed models or exploiting low-rank tensor representations to improve reconstruction accuracy and generalization across different imaging scenarios. These advancements offer substantial improvements in patient comfort, workflow efficiency, and the potential for real-time applications in areas like interventional MRI and speech analysis, impacting both clinical practice and research.