Medical Image Translation

Medical image translation aims to automatically convert images from one modality (e.g., MRI to CT) to another, improving diagnostic capabilities and workflow efficiency. Current research heavily utilizes generative models, particularly diffusion models and generative adversarial networks (GANs), often incorporating techniques like attention mechanisms, frequency-domain processing, and cycle consistency to enhance image quality and anatomical accuracy. These advancements address challenges such as handling noisy data, preserving fine structural details, and improving the evaluation of translation performance through better metrics. The ultimate goal is to provide clinicians with more comprehensive and readily accessible imaging data, facilitating improved diagnosis and treatment planning.