Validation of the automated method VIENA: an accurate, precise, and robust measure of ventricular enlargement.
BACKGROUND
In many retrospective studies and large clinical trials, high-resolution, good-contrast 3DT1 images are unavailable, hampering detailed analysis of brain atrophy. Ventricular enlargement then provides a sensitive indirect measure of ongoing central brain atrophy. Validated automated methods are required that can reliably measure ventricular enlargement and are robust across magnetic resonance (MR) image types.


OBJECTIVE
To validate the automated method VIENA for measuring the percentage ventricular volume change (PVVC) between two scans.


METHODS
Accuracy was assessed using four image types, acquired in 15 elderly patients (five with Alzheimer's disease, five with mild cognitive impairment, and five cognitively normal elderly) and 58 patients with multiple sclerosis (MS), by comparing PVVC values from VIENA to manual outlining. Precision was assessed from data with three imaging time points per MS patient, by measuring the difference between the direct (one-step) and indirect (two-step) measurement of ventricular volume change between the first and last time points. The stringent concordance correlation coefficient (CCC) was used to quantify absolute agreement.


RESULTS
CCC of VIENA with manual measurement was 0.84, indicating good absolute agreement. The median absolute difference between two-step and one-step measurement with VIENA was 1.01%, while CCC was 0.98. Neither initial ventricular volume nor ventricular volume change affected performance of the method.


CONCLUSIONS
VIENA has good accuracy and good precision across four image types. VIENA therefore provides a useful fully automated method for measuring ventricular volume change in large datasets.


CONCLUSIONS
VIENA is a robust, accurate, and precise method for measuring ventricular volume change.
Department of Radiology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands; Department of Physics and Medical Technology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
Hugo
H
Vrenken
Eline K
EK
Vos
W M
WM
van der Flier
Ingrid C
IC
Sluimer
Keith S
KS
Cover
Dirk L
DL
Knol
Frederik
F
Barkhof
D000375:Aging / Q000473:pathology; D000544:Alzheimer Disease / Q000473:pathology; D001284:Atrophy / Q000473:pathology; D002552:Cerebral Ventricles / Q000473:pathology*; D060825:Cognitive Dysfunction / Q000473:pathology; D018450:Disease Progression; D001330:Electronic Data Processing; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000473:pathology; D009929:Organ Size; D013997:Time Factors
3D imaging; Alzheimer disease; atrophy; brain; computer-assisted image analysis; magnetic resonance imaging; multiple sclerosis