Multi Modal MRI

Multimodal MRI leverages data from multiple MRI sequences to improve diagnostic accuracy and treatment planning by integrating complementary information about tissue properties. Current research focuses on optimizing data fusion strategies within deep learning models, including exploring various architectures like UNets and Transformers, and developing methods for handling incomplete or imperfectly registered datasets. These advancements aim to enhance the robustness and accuracy of automated segmentation and classification tasks for various pathologies, ultimately improving clinical workflows and patient care. The field is also actively investigating explainability methods to increase the transparency and trustworthiness of these powerful AI tools in medical applications.