Anisotropic Magnetic Resonance

Anisotropic magnetic resonance (MR) imaging produces images with differing resolutions along different axes, hindering analysis and visualization. Current research heavily focuses on developing deep learning-based super-resolution methods to reconstruct isotropic (equal resolution) images from anisotropic data, employing architectures like U-net, Vision Transformers, and CycleGAN variations. These self-supervised or weakly-supervised approaches aim to overcome the limitations of anisotropic data without requiring paired high-resolution training data, improving the quality and usability of MR images in various medical applications. This work has significant implications for improving diagnostic accuracy and efficiency in medical imaging.