Accelerated Magnetic Resonance Imaging

Accelerated magnetic resonance imaging (MRI) aims to significantly reduce scan times by acquiring incomplete k-space data and reconstructing the missing information. Current research heavily utilizes deep learning, employing various architectures like convolutional neural networks (CNNs), unrolled networks, and normalizing flows, often incorporating techniques such as knowledge distillation and reinforcement learning to optimize both data acquisition and reconstruction processes. These advancements promise faster, more efficient MRI scans, improving patient comfort and potentially expanding the accessibility and applications of this crucial medical imaging modality.