Multi Modality Image

Multi-modality image analysis focuses on integrating information from different imaging modalities (e.g., MRI, CT, PET, infrared, visible light) to improve diagnostic accuracy and treatment planning in various applications, primarily medical imaging. Current research emphasizes developing deep learning models, including generative adversarial networks (GANs), diffusion models, and self-supervised learning architectures, to effectively fuse and analyze these diverse data sources, often addressing challenges like data scarcity and modality discrepancies through techniques such as masked autoencoders and optimal transport. This field is significantly impacting healthcare by enabling more accurate disease detection, improved image reconstruction, and more efficient image registration for tasks such as radiotherapy planning, ultimately leading to better patient outcomes.