White Matter
White matter, the brain's network of connecting fibers, is a crucial focus of neuroimaging research aiming to understand brain structure and function in health and disease. Current research heavily utilizes diffusion MRI (dMRI) coupled with advanced machine learning techniques, including deep neural networks (e.g., U-Net, Transformers, autoencoders) and graph convolutional networks, to improve the accuracy and efficiency of white matter tract segmentation, parcellation, and analysis. These advancements enable more precise quantification of white matter microstructure, connectivity, and geometry, leading to improved diagnostic capabilities for neurological disorders and a deeper understanding of brain-behavior relationships. The development of robust and generalizable methods, particularly for handling diverse datasets and addressing challenges like noise and artifacts in dMRI data, remains a key area of ongoing investigation.
Papers
Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions
Tengfei Xue, Fan Zhang, Chaoyi Zhang, Yuqian Chen, Yang Song, Alexandra J. Golby, Nikos Makris, Yogesh Rathi, Weidong Cai, Lauren J. O'Donnell
Segmenting white matter hyperintensities on isotropic three-dimensional Fluid Attenuated Inversion Recovery magnetic resonance images: Assessing deep learning tools on norwegian imaging database
Martin Soria Røvang, Per Selnes, Bradley John MacIntosh, Inge Rasmus Groote, Lene Paalhaugen, Sudre Carole, Tormod Fladby, Atle Bjørnerud