Diffusion Imaging Is a Very Programmable Modality

Guest: Anastasia Yendiki is a faculty member at the MGH Martinos center and a member of the Laboratory for Computational Neuroimaging (LCN).  Her background is in statistical signal and image processing. She received her Ph.D. in Electrical Engineering from the University of Michigan at Ann Arbor, where she worked on inverse problems in tomographic reconstruction for nuclear imaging. As a postdoctoral research fellow at the Martinos Center, Harvard Medical School and Massachusetts General Hospital, she trained in functional and diffusion-weighted MRI. She is responsible for the development of the diffusion MRI analysis tools in FreeSurfer, including TRACULA (TRActs Constrained by UnderLying Anatomy), a diffusion-weighed MRI analysis stream in Bruce Fischl’s FreeSurfer, for automatically reconstructing a set of major white matter pathways from diffusion MRI data using global probabilistic tractography with anatomical priors. She is also interested in ex vivo imaging of human brain circuits with diffusion MRI and optical imaging to both validate and train algorithms for in vivo tractography.

Discussion

In this wide-reaching discussion we delve into all aspects of her work developing diffusion-based tractography, including her work on better algorithms, current unknowns and challenges, her validation studies, clinical applications, and Connectome scanner at MGH. Towards the end we discuss the planned connectome II scanner and some of the most exciting challenges the field faces.