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Automatic Quantitative Left Ventricular Analysis of Cine MR Images by Using Three-dimensional Information for Contour Detection¹

¹ From the Departments of Cardiology (R.J.M.v.G., T.B., P.J.d.F.) and Radiology (R.J.M.v.G., T.B., P.A.W., F.C., P.J.d.F.), Erasmus Medical Center, Thoraxcenter Ba 585, PO Box 2040, 3000 CA Rotterdam, the Netherlands; Department of Medical Informatics, Maastricht University, Maastricht, the Netherlands (E.H.B.M.G.); and Pie Medical Imaging, Maastricht, the Netherlands (J.P.M.M.A.). Received March 21, 2005; revision requested May 12; revision received July 10; accepted July 27; final version accepted September 14. Address correspondence to R.J.M.v.G. (e-mail: r.vangeuns{at}erasmusmc.nl).

The purpose of this study was to evaluate an automatic boundary detection algorithm of the left ventricle on magnetic resonance (MR) short-axis images with the essential restriction of no manual corrections. The study comprised 13 patients (nine men, four women) and 12 healthy volunteers (11 men, one woman), and institutional review board approval and informed consent were obtained. The outline of the left ventricle was indicated manually on horizontal and vertical long-axis MR images. The calculated intersection points with the short-axis MR images were the basis of the automatic contour detection. Automatically derived volumes correlated highly with manually derived (short axis–based) volumes (R² = 0.98); ejection fraction (EF) and mass showed a correlation of 0.95 and 0.93, respectively. Automatic contour detection reduced interobserver variability to 0.1 mL for endocardial end-diastolic and end-systolic volumes, 1.1 mL for epicardial end-diastolic and end-systolic volumes, 0.02% for EF, and 1.1 g for mass. Thus, the algorithm enabled highly reproducible left ventricular parameters to be obtained.

© RSNA, 2006

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