Diffusion Weighted MRI

Diffusion-weighted MRI (DWI) is a neuroimaging technique used to probe tissue microstructure by measuring the diffusion of water molecules, providing insights into tissue structure and integrity. Current research focuses on improving DWI analysis through advanced deep learning models, including convolutional neural networks and transformers, to enhance accuracy and efficiency in tasks such as lesion segmentation, parameter estimation (e.g., apparent diffusion coefficient, diffusion tensor), and prediction of clinical outcomes. These advancements are significantly impacting various fields, enabling more accurate diagnosis and prognosis of conditions like stroke and cancer, as well as facilitating improved monitoring of fetal brain and lung development. The development of robust and generalizable algorithms, coupled with improved motion correction techniques, is crucial for translating DWI's potential into widespread clinical applications.