Dynamic Magnetic Resonance Imaging

Dynamic Magnetic Resonance Imaging (dMRI) aims to capture high-resolution images of moving organs and tissues, but faces challenges in balancing speed and image quality due to long acquisition times. Current research focuses on accelerating dMRI acquisition and reconstruction using compressed sensing (CS) techniques and deep learning models, including generative adversarial networks (GANs), diffusion models, and various convolutional neural networks (CNNs) such as U-Nets, often incorporating spatiotemporal priors and unrolled optimization algorithms. These advancements promise to significantly improve the speed and diagnostic value of dMRI, impacting various medical fields by enabling faster, more comfortable, and potentially more accurate diagnoses.