Magnetic Resonance

Magnetic resonance (MR) techniques, encompassing MRI and MRS, aim to produce high-quality images and spectra for medical diagnosis and scientific research. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), transformers, and generative adversarial networks (GANs) to address challenges such as image reconstruction from undersampled data, noise reduction, and automated segmentation of anatomical structures. These advancements improve diagnostic accuracy, accelerate data processing, and enable quantitative analysis, impacting various fields from clinical radiology to materials science. The development of robust uncertainty quantification methods further enhances the reliability and trustworthiness of MR-based analyses.