Magnetic Resonance Imaging Reconstruction

Magnetic resonance imaging (MRI) reconstruction aims to create high-quality images from incomplete or noisy measurement data, accelerating scan times and improving image quality. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks, generative models (e.g., diffusion models), and unrolled optimization methods to improve robustness and efficiency, often incorporating techniques like randomized smoothing to mitigate sensitivity to noise. These advancements are crucial for enhancing diagnostic capabilities in clinical settings by enabling faster, higher-resolution, and more robust MRI scans, ultimately improving patient care and research outcomes.