Accelerated MRI

Accelerated MRI aims to significantly reduce scan times by acquiring incomplete k-space data and reconstructing high-quality images using advanced computational methods. Current research heavily utilizes deep learning, employing architectures like diffusion models, transformers, and convolutional neural networks, often integrated with physics-based constraints to improve reconstruction accuracy and robustness to noise. These advancements are crucial for improving patient comfort, enabling real-time imaging, and expanding the clinical applicability of MRI, particularly in applications like intraoperative imaging and high-resolution 3D scans. The field is also exploring self-supervised learning techniques to reduce reliance on fully-sampled training data.