BACKGROUND: T2-weighted cardiovascular magnetic resonance (CMR) has been shown to be a promising technique for determination of ischemic myocardium, referred to as myocardium at risk (MaR), after an acute coronary event. Quantification of MaR in T2-weighted CMR has been proposed to be performed by manual delineation or the threshold methods of two standard deviations from remote (2SD), full width half maximum intensity (FWHM) or Otsu. However, manual delineation is subjective and threshold methods have inherent limitations related to threshold definition and lack of a priori information about cardiac anatomy and physiology. Therefore, the aim of this study was to develop an automatic segmentation algorithm for quantification of MaR using anatomical a priori information. METHODS: Forty-seven patients with first-time acute ST-elevation myocardial infarction underwent T2-weighted CMR within 1 week after admission. Endocardial and epicardial borders of the left ventricle, as well as the hyper enhanced MaR regions were manually delineated by experienced observers and used as reference method. A new automatic segmentation algorithm, called Segment MaR, defines the MaR region as the continuous region most probable of being MaR, by estimating the intensities of normal myocardium and MaR with an expectation maximization algorithm and restricting the MaR region by an a priori model of the maximal extent for the user defined culprit artery. The segmentation by Segment MaR was compared against inter observer variability of manual delineation and the threshold methods of 2SD, FWHM and Otsu. RESULTS: MaR was 32.9 ± 10.9% of left ventricular mass (LVM) when assessed by the reference observer and 31.0 ± 8.8% of LVM assessed by Segment MaR. The bias and correlation was, -1.9 ± 6.4% of LVM, R = 0.81 (p < 0.001) for Segment MaR, -2.3 ± 4.9%, R = 0.91 (p < 0.001) for inter observer variability of manual delineation, -7.7 ± 11.4%, R = 0.38 (p = 0.008) for 2SD, -21.0 ± 9.9%, R = 0.41 (p = 0.004) for FWHM, and 5.3 ± 9.6%, R = 0.47 (p < 0.001) for Otsu. CONCLUSIONS: There is a good agreement between automatic Segment MaR and manually assessed MaR in T2-weighted CMR. Thus, the proposed algorithm seems to be a promising, objective method for standardized MaR quantification in T2-weighted CMR.
BACKGROUND: T2-weighted cardiovascular magnetic resonance (CMR) has been shown to be a promising technique for determination of ischemic myocardium, referred to as myocardium at risk (MaR), after an acute coronary event. Quantification of MaR in T2-weighted CMR has been proposed to be performed by manual delineation or the threshold methods of two standard deviations from remote (2SD), full width half maximum intensity (FWHM) or Otsu. However, manual delineation is subjective and threshold methods have inherent limitations related to threshold definition and lack of a priori information about cardiac anatomy and physiology. Therefore, the aim of this study was to develop an automatic segmentation algorithm for quantification of MaR using anatomical a priori information. METHODS: Forty-seven patients with first-time acute ST-elevation myocardial infarction underwent T2-weighted CMR within 1 week after admission. Endocardial and epicardial borders of the left ventricle, as well as the hyper enhanced MaR regions were manually delineated by experienced observers and used as reference method. A new automatic segmentation algorithm, called Segment MaR, defines the MaR region as the continuous region most probable of being MaR, by estimating the intensities of normal myocardium and MaR with an expectation maximization algorithm and restricting the MaR region by an a priori model of the maximal extent for the user defined culprit artery. The segmentation by Segment MaR was compared against inter observer variability of manual delineation and the threshold methods of 2SD, FWHM and Otsu. RESULTS: MaR was 32.9 ± 10.9% of left ventricular mass (LVM) when assessed by the reference observer and 31.0 ± 8.8% of LVM assessed by Segment MaR. The bias and correlation was, -1.9 ± 6.4% of LVM, R = 0.81 (p < 0.001) for Segment MaR, -2.3 ± 4.9%, R = 0.91 (p < 0.001) for inter observer variability of manual delineation, -7.7 ± 11.4%, R = 0.38 (p = 0.008) for 2SD, -21.0 ± 9.9%, R = 0.41 (p = 0.004) for FWHM, and 5.3 ± 9.6%, R = 0.47 (p < 0.001) for Otsu. CONCLUSIONS: There is a good agreement between automatic Segment MaR and manually assessed MaR in T2-weighted CMR. Thus, the proposed algorithm seems to be a promising, objective method for standardized MaR quantification in T2-weighted CMR.
Authors: Manuel D Cerqueira; Neil J Weissman; Vasken Dilsizian; Alice K Jacobs; Sanjiv Kaul; Warren K Laskey; Dudley J Pennell; John A Rumberger; Thomas Ryan; Mario S Verani Journal: Circulation Date: 2002-01-29 Impact factor: 29.690
Authors: Alexander Stork; Kai Muellerleile; Paul M Bansmann; Joachim Graessner; Michael Kaul; Joern Kemper; Gerhard Adam; Gunnar K Lund Journal: Eur Radiol Date: 2006-12-06 Impact factor: 5.315
Authors: Li-Yueh Hsu; Alex Natanzon; Peter Kellman; Glenn A Hirsch; Anthony H Aletras; Andrew E Arai Journal: J Magn Reson Imaging Date: 2006-03 Impact factor: 4.813
Authors: Hassan Abdel-Aty; Anja Zagrosek; Jeanette Schulz-Menger; Andrew J Taylor; Daniel Messroghli; Andreas Kumar; Michael Gross; Rainer Dietz; Matthias G Friedrich Journal: Circulation Date: 2004-05-03 Impact factor: 29.690
Authors: Dudley J Pennell; A John Baksi; John Paul Carpenter; David N Firmin; Philip J Kilner; Raad H Mohiaddin; Sanjay K Prasad Journal: J Cardiovasc Magn Reson Date: 2013-09-04 Impact factor: 5.364
Authors: Jamal N Khan; Sheraz A Nazir; Mark A Horsfield; Anvesha Singh; Prathap Kanagala; John P Greenwood; Anthony H Gershlick; Gerry P McCann Journal: BMC Res Notes Date: 2015-02-25
Authors: Henrik Engblom; Jane Tufvesson; Robert Jablonowski; Marcus Carlsson; Anthony H Aletras; Pavel Hoffmann; Alexis Jacquier; Frank Kober; Bernhard Metzler; David Erlinge; Dan Atar; Håkan Arheden; Einar Heiberg Journal: J Cardiovasc Magn Reson Date: 2016-05-04 Impact factor: 5.364
Authors: Jane Tufvesson; Marcus Carlsson; Anthony H Aletras; Henrik Engblom; Jean-François Deux; Sasha Koul; Peder Sörensson; John Pernow; Dan Atar; David Erlinge; Håkan Arheden; Einar Heiberg Journal: BMC Med Imaging Date: 2016-03-05 Impact factor: 1.930