MRI Synthesis

MRI synthesis uses deep learning to generate missing or incomplete MRI scans from existing data, aiming to improve diagnostic accuracy and reduce the need for multiple, time-consuming scans. Current research focuses on developing sophisticated generative models, such as diffusion models and GANs, often incorporating techniques like contrastive learning and multi-modal conditioning to improve image fidelity and control over synthesis. This technology has significant implications for clinical practice by potentially expanding access to comprehensive MRI data and enhancing the efficiency of diagnostic workflows, particularly in resource-constrained settings.