Diffusion MRI
Diffusion MRI (dMRI) is a neuroimaging technique used to map the brain's microstructure and connectivity by measuring the diffusion of water molecules. Current research focuses on improving dMRI data quality through techniques like deep generative models (e.g., diffusion models, U-Nets) for tasks such as field-of-view extension, denoising, and super-resolution, often incorporating multi-modal data and physics-informed approaches. These advancements aim to enhance the accuracy and efficiency of dMRI-based analyses, including tractography, brain parcellation, and microstructure estimation, ultimately improving the diagnosis and treatment of neurological disorders and advancing our understanding of brain structure and function. The development of open-source datasets and standardized evaluation metrics is also a significant area of focus.
Papers
Optimizing Synthetic Correlated Diffusion Imaging for Breast Cancer Tumour Delineation
Chi-en Amy Tai, Alexander Wong
Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging
Chi-en Amy Tai, Alexander Wong
Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction
Chi-en Amy Tai, Alexander Wong