Automated brain tissue assessment in the elderly and demented population: construction and validation of a sub-volume probabilistic brain atlas.
OBJECTIVE
To develop an automated imaging assessment tool that accommodates the anatomic variability of the elderly and demented population as well as the registration errors occurring during spatial normalization.


METHODS
20 subjects with Alzheimer's disease (AD), mild cognitive impairment, or normal cognition underwent MRI brain imaging and had their 3D volumetric datasets manually partitioned into 68 regions of interest (ROI) termed sub-volumes. Gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) voxel counts were then made in the subject's native space for comparison against automated volumetric measures within three sub-volume probabilistic atlas (SVPA) models. The three SVPAs were constructed using 12 parameter affine (12 p), 2nd order (2nd), and 6th order (6th) transforms derived from registering the manually partitioned scans into a Talairach compatible AD population-based target. The three SVPA automated measures were compared to the manually derived measures in the 20 subjects' native space with a "jack-knife" procedure in which each subject was assessed by an SVPA they did not contribute toward constructing.


RESULTS
The mean left and right GM ratio (GM ratio = [GM + CSF] / CSF) "r values" for the 3 SVPAs compared to the manually derived ratios across the 68 ROIs were 0.85 for the 12p SVPA, 0.88 for the 2nd SVPA, and 0.89 for the 6th SVPA. The mean left and right WM ratio (WM ratio = [WM + CSF] / CSF) "r values" for the 3 SVPAs being 0.84 for the 12p SVPA, 0.86 for the 2nd SVPA, and 0.88 for the 6th SVPA.


CONCLUSIONS
We have constructed, from an elderly and demented cohort, an automated brain volumetric tool that has excellent accuracy compared to a manual gold standard and is capable of regional hypothesis testing and individual patient assessment compared to a population.
Mega
Michael S
MS
Alzheimer's Disease Center, Providence Health System, 10150 SE 32nd Avenue, Portland, OR 97222, USA. michael.mega@providence.org
Dinov
Ivo D
ID
Mazziotta
John C
JC
Manese
Mario
M
Thompson
Paul M
PM
Lindshield
Chris
C
Moussai
Jacob
J
Tran
Nah
N
Olsen
Kirsten
K
Zoumalan
Chris I
CI
Woods
Roger P
RP
Toga
Arthur W
AW
D000368:Aged; D000375:Aging / Q000473:pathology*; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology; D001921:Brain / Q000033:anatomy & histology* / Q000254:growth & development / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D003627:Data Interpretation, Statistical; D003704:Dementia / Q000473:pathology*; D007839:Functional Laterality / Q000502:physiology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data; D020534:Parahippocampal Gyrus / Q000254:growth & development / Q000473:pathology; D012016:Reference Values