TY - JOUR
T1 - 76-Space analysis of grey matter diffusivity
T2 - Methods and applications
AU - Liu, Tianming
AU - Young, Geoffrey
AU - Huang, Ling
AU - Chen, Nan Kuei
AU - Wong, Stephen T.C.
N1 - Funding Information:
This research work is supported by a grant to Dr. Wong from the Harvard Center for Neurodegeneration and Repair (HCNR), Harvard Medical School. The normal control datasets are from the NIH sponsored NAMIC (National Alliance of Medical Image Computing) data-repository and are provided by the Laboratory of Neuroscience, Department of Psychiatry, Boston VA Healthcare System and Harvard Medical School, which is supported by following grants: NIMH R01 MH50740 (Shenton), NIH K05 MH01110 (Shenton), NIMH R01 MH52807 (McCarley), NIMH R01 MH40799 (McCarley), VA Merit Awards (Shenton; McCarley) and VA Research Enhancement Award Program (REAP: McCarley). We would like to thank Ms. Yi-ru Lin of HCNR Center for Bioinformatics for manual labeling of selected datasets, Dr. Susumu Mori of the Johns Hopkins University for sharing his DTI datasets, Dr. Noor Kabani of the Montreal Neurological Institute for sharing the brain atlas and Dr. Kelvin Wong for helpful discussions in the revision of this paper.
PY - 2006/5/15
Y1 - 2006/5/15
N2 - Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) allow in vivo investigation of molecular motion of tissue water at a microscopic level in cerebral gray matter (GM) and white matter (WM). DWI/DTI measure of water diffusion has been proven to be invaluable for the study of many neurodegenerative diseases (e.g., Alzheimer's disease and Creutzfeldt-Jakob disease) that predominantly involve GM. Thus, quantitative analysis of GM diffusivity is of scientific interest and is promised to have a clinical impact on the investigation of normal brain aging and neuropathology. In this paper, we propose an automated framework for analysis of GM diffusivity in 76 standard anatomic subdivisions of gray matter to facilitate studies of neurodegenerative and other gray matter neurological diseases. The computational framework includes three enabling technologies: (1) automatic parcellation of structural MRI GM into 76 precisely defined neuroanatomic subregions ("76-space"), (2) automated segmentation of GM, WM and CSF based on DTI data, and (3) automatic measurement of the average apparent diffusion coefficient (ADC) in each segmented GM subregion. We evaluate and validate this computational framework for 76-space GM diffusivity analysis using data from normal volunteers and from patients with Creutzfeldt-Jakob disease.
AB - Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) allow in vivo investigation of molecular motion of tissue water at a microscopic level in cerebral gray matter (GM) and white matter (WM). DWI/DTI measure of water diffusion has been proven to be invaluable for the study of many neurodegenerative diseases (e.g., Alzheimer's disease and Creutzfeldt-Jakob disease) that predominantly involve GM. Thus, quantitative analysis of GM diffusivity is of scientific interest and is promised to have a clinical impact on the investigation of normal brain aging and neuropathology. In this paper, we propose an automated framework for analysis of GM diffusivity in 76 standard anatomic subdivisions of gray matter to facilitate studies of neurodegenerative and other gray matter neurological diseases. The computational framework includes three enabling technologies: (1) automatic parcellation of structural MRI GM into 76 precisely defined neuroanatomic subregions ("76-space"), (2) automated segmentation of GM, WM and CSF based on DTI data, and (3) automatic measurement of the average apparent diffusion coefficient (ADC) in each segmented GM subregion. We evaluate and validate this computational framework for 76-space GM diffusivity analysis using data from normal volunteers and from patients with Creutzfeldt-Jakob disease.
UR - http://www.scopus.com/inward/record.url?scp=33646589721&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33646589721&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2005.11.041
DO - 10.1016/j.neuroimage.2005.11.041
M3 - Article
C2 - 16434215
AN - SCOPUS:33646589721
SN - 1053-8119
VL - 31
SP - 51
EP - 65
JO - NeuroImage
JF - NeuroImage
IS - 1
ER -