Surface-based parcellation and vertex-wise analysis of ultra
  high-resolution ex vivo 7 tesla MRI in neurodegenerative diseases
Pulkit Khandelwal Michael Tran Duong Constanza Fuentes Amanda Denning Winifred Trotman Ranjit Ittyerah Alejandra Bahena Theresa Schuck Marianna Gabrielyan Karthik Prabhakaran Daniel Ohm Gabor Mizsei John Robinson Monica Munoz John Detre Edward Lee David Irwin Corey McMillan M. Dylan Tisdall Sandhitsu Das David Wolk Paul A. Yushkevich
Magnetic resonance imaging (MRI) is the standard modality to understand human
brain structure and function in vivo (antemortem). Decades of research in human
neuroimaging has led to the widespread development of methods and tools to
provide automated volume-based segmentations and surface-based parcellations
which help localize brain functions to specialized anatomical regions. Recently
ex vivo (postmortem) imaging of the brain has opened-up avenues to study brain
structure at sub-millimeter ultra high-resolution revealing details not
possible to observe with in vivo MRI. Unfortunately, there has been limited
methodological development in ex vivo MRI primarily due to lack of datasets and
limited centers with such imaging resources. Therefore, in this work, we
present one-of-its-kind dataset of 82 ex vivo T2w whole brain hemispheres MRI
at 0.3 mm isotropic resolution spanning Alzheimer's disease and related
dementias. We adapted and developed a fast and easy-to-use automated
surface-based pipeline to parcellate, for the first time, ultra high-resolution
ex vivo brain tissue at the native subject space resolution using the
Desikan-Killiany-Tourville (DKT) brain atlas. This allows us to perform
vertex-wise analysis in the template space and thereby link morphometry
measures with pathology measurements derived from histology. We will
open-source our dataset docker container, Jupyter notebooks for ready-to-use
out-of-the-box set of tools and command line options to advance ex vivo MRI
clinical brain imaging research on the project webpage.