A semi-automatic technique for measurement of arterial wall from black blood MRI.
Black blood magnetic resonance imaging (MRI) has become a popular technique for imaging the artery wall in vivo. Its noninvasiveness and high resolution make it ideal for studying the progression of early atherosclerosis in normal volunteers or asymptomatic patients with mild disease. However, the operator variability inherent in the manual measurement of vessel wall area from MR images hinders the reliable detection of relatively small changes in the artery wall over time. In this paper we present a semi-automatic method for segmenting the inner and outer boundary of the artery wall, and evaluate its operator variability using analysis of variance (ANOVA). In our approach, a discrete dynamic contour is approximately initialized by an operator, deformed to the inner boundary, dilated, and then deformed to the outer boundary. A group of four operators performed repeated measurements on 12 images from normal human subjects using both our semiautomatic technique and a manual approach. Results from the ANOVA indicate that the inter-operator standard error of measurement (SEM) of total wall area decreased from 3.254 mm2 (manual) to 1.293 mm2 (semi-automatic), and the intra-operator SEM decreased from 3.005 mm2 to 0.958 mm2. Operator reliability coefficients increased from less than 69% to more than 91% (inter-operator) and 95% (intra-operator). The minimum detectable change in wall area improved from more than 8.32 mm2 (intra-operator, manual) to less than 3.59 mm2 (inter-operator, semi-automatic), suggesting that it is better to have multiple operators measure wall area with our semi-automatic technique than to have a single operator make repeated measurements manually. Similar improvements in wall thickness and lumen radius measurements were also recorded. Since the semi-automatic technique has effectively ruled out the effect of the operator on these measurements, it may be possible to use such techniques to expand prospective studies of atherogenesis to multiple centers so as to increase access to real patient data without sacrificing reliability.
Ladak
H M
HM
Imaging Research Labs, John P. Robarts Research Institute, and Departments of Medical Biophysics and Electrical and Computer Engineering, University of Western Ontario, London N6A 5K8, Canada.
Thomas
J B
JB
Mitchell
J R
JR
Rutt
B K
BK
Steinman
D A
DA
D000328:Adult; D000465:Algorithms; D000704:Analysis of Variance; D001158:Arteries / Q000033:anatomy & histology*; D001161:Arteriosclerosis / Q000175:diagnosis / Q000209:etiology; D055592:Biophysical Phenomena; D001703:Biophysics; D001769:Blood; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data; D008297:Male; D011446:Prospective Studies