Magnetic Resonance Image Reconstruction

Magnetic resonance image (MRI) reconstruction aims to create high-quality images from incomplete or noisy data, accelerating scan times and improving patient comfort. Current research heavily utilizes deep learning, employing various architectures like convolutional neural networks (CNNs), transformers, and diffusion models, often within optimization frameworks or plug-and-play schemes to improve reconstruction accuracy and robustness. These advancements focus on addressing challenges such as memory efficiency, model compression via knowledge distillation, and handling uncertainties inherent in undersampled data, ultimately striving for improved diagnostic image quality and faster MRI acquisition.