Automated detection of regional wall motion abnormalities based on a statistical model applied to multislice short-axis cardiac MR images.
In this paper, a statistical shape analysis method for myocardial contraction is presented that was built to detect and locate regional wall motion abnormalities (RWMA). For each slice level (base, middle, and apex), 44 short-axis magnetic resonance images were selected from healthy volunteers to train a statistical model of normal myocardial contraction using independent component analysis (ICA). A classification algorithm was constructed from the ICA components to automatically detect and localize abnormally contracting regions of the myocardium. The algorithm was validated on 45 patients suffering from ischemic heart disease. Two validations were performed; one with visual wall motion scores (VWMS) and the other with wall thickening (WT) used as references. Accuracy of the ICA-based method on each slice level was 69.93% (base), 89.63% (middle), and 72.78% (apex) when WT was used as reference, and 63.70% (base), 67.41% (middle), and 66.67% (apex) when VWMS was used as reference. From this we conclude that the proposed method is a promising diagnostic support tool to assist clinicians in reducing the subjectivity in VWMS.
Suinesiaputra
Avan
A
Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands.
Frangi
Alejandro F
AF
Kaandorp
Theodorus A M
TA
Lamb
Hildo J
HJ
Bax
Jeroen J
JJ
Reiber
Johan H C
JH
Lelieveldt
Boudewijn P F
BP
D000465:Algorithms; D003627:Data Interpretation, Statistical; D005260:Female; D006321:Heart / Q000503:physiopathology; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008955:Models, Cardiovascular; D015233:Models, Statistical; D009200:Myocardial Contraction; D017202:Myocardial Ischemia / Q000503:physiopathology; D009206:Myocardium / Q000473:pathology*; D012372:ROC Curve; D015203:Reproducibility of Results