Automated Detection of Regional Wall Motion Abnormalities Based on a Statistical Model Applied to Multislice Short-Axis Cardiac MR Images

• Published in: IEEE transactions on medical imaging Vol. 28; no. 4; pp. 595 - 607
• Main Authors: Suinesiaputra, A., Frangi, A.F., Kaandorp, T., Lamb, H.J., Bax, J.J., Reiber, J., Lelieveldt, B.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-04-2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cardiac disease; Classification algorithms; Data Interpretation, Statistical; Female; Heart - physiopathology; Humans; Image motion analysis; Independent component analysis; Independent component analysis (ICA); Magnetic analysis; Magnetic resonance; Magnetic Resonance Imaging; Male; medical diagnosis; Models, Cardiovascular; Models, Statistical; Motion analysis; Motion detection; Myocardial Contraction; Myocardial Ischemia - physiopathology; Myocardium; Myocardium - pathology; NMR; Nuclear magnetic resonance; pattern classification; regional wall motion abnormality (RWMA); Reproducibility of Results; ROC Curve; Shape; statistical shape analysis
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2008.2008966

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.