Multi Sequence MRI

Multi-sequence MRI research focuses on leveraging the complementary information from multiple MRI scans of the same subject to improve diagnostic accuracy and treatment planning. Current research employs generative adversarial networks (GANs), autoencoders with vector-quantized latent spaces, and transformer architectures to synthesize missing sequences, enhance image resolution, and learn robust representations across diverse acquisition parameters. These advancements enable improved disease prediction, personalized treatment strategies, and more reliable analyses, particularly in neurodegenerative diseases like multiple sclerosis and Alzheimer's disease, by mitigating the impact of missing or inconsistent data.