Automated detection of regional wall motion abnormalities based on a statistical model applied to multislice short-axis cardiac MR images

Avan Suinesiaputra, Alejandro F. Frangi, Theodorus A. M. Kaandorp, Hildo J. Lamb, Jeroen J. Bax, Johan H. C. Reiber, Boudewijn P. F. Lelieveldt

Research output: Contribution to journalArticlepeer-review

Abstract

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
Original languageEnglish
Pages (from-to)595-607
Number of pages13
JournalIEEE Transactions on Medical Imaging
Volume28
Issue number4
DOIs
Publication statusPublished - 2009

Keywords

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