Fast Magnetic Resonance Imaging Reconstruction

Fast magnetic resonance imaging (MRI) reconstruction aims to significantly reduce scan times by acquiring undersampled k-space data and then reconstructing high-quality images using computational methods. Current research heavily utilizes deep learning, employing architectures like transformers, generative adversarial networks (GANs), and convolutional neural networks (CNNs), often incorporating physics-informed priors or attention mechanisms to improve accuracy and preserve fine details such as edges. These advancements are crucial for improving patient comfort, reducing motion artifacts, and enabling faster clinical workflows, ultimately increasing the accessibility and efficiency of MRI in healthcare.