Automated myocardial edge detection on MR images: accuracy in consecutive subjects.
The authors previously demonstrated the feasibility of graph-searching-based automated edge detection in cardiac magnetic resonance (MR) imaging. To further assess the clinical utility of this method, unselected images from 11 consecutive subjects undergoing clinically indicated (except for one healthy volunteer) short-axis spin-echo MR imaging were analyzed. A total of 142 images from the 11 subjects, encompassing the left ventricle from apex to outflow tract, were analyzed. The computer algorithm correctly identified complete endocardial and epicardial contours in 121 of 142 images (85%). Correlations between observer-traced and computer-derived epicardial areas for all images were good (r = .71 for epicardium, r = .83 for endocardium); they improved for a subset of higher-quality images (r = .82 for epicardium, r = .92 for endocardium). The authors conclude that the current data further support the usefulness of computer digital image processing in geometric analysis of cardiac MR image data.
Department of Internal Medicine, University of Iowa, Iowa City 52242-1182.
S R
SR
Fleagle
D R
DR
Thedens
W
W
Stanford
B H
BH
Thompson
J M
JM
Weston
P P
PP
Patel
D J
DJ
Skorton
D000293:Adolescent; D000328:Adult; D000368:Aged; D004699:Endocardium / Q000473:pathology; D006331:Heart Diseases / Q000175:diagnosis; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D009206:Myocardium / Q000473:pathology*; D010496:Pericardium / Q000473:pathology; D012189:Retrospective Studies