We present an automated technique using noise-based level-set methods and non-rigid registration for myocardial detection as a basis for perfusion quantification from cardiac magnetic resonance (CMR) images. We studied 27 patients undergoing contrast-enhanced CMR imaging (1.5T) at rest and during adenosine stress, and validated our technique against conventional manual analysis both directly and using quantitative coronary angiography (QCA) as reference. Contrast enhancement time-curves were constructed and used to calculate a number of perfusion indices. Measured segmental pixel intensities in each frame correlated highly with manual analysis (r=0.95). Bland-Altman analysis showed small biases (1.3 at rest; 0.0 at stress) and narrow limits of agreement (±13 at rest; ±14 at stress). The derived perfusion indices showed the same diagnostic accuracy as manual analysis (AUC up to 0.71 vs. 0.70). These results indicate that our automated technique allows fast detection of myocardial ROIs and quantification of perfusion abnormalities as accurately as manual analysis.

G. Tarroni, C. Corsi, P.F. Antkowiak, F. Veronesi, C.M. Kramer, F.H. Epstein, et al. (2012). Automated Segmentation and Non-rigid Registration for MRI-Based Quantification of Myocardial First-Pass Contrast Enhancement. BOLOGNA : Patron.

Automated Segmentation and Non-rigid Registration for MRI-Based Quantification of Myocardial First-Pass Contrast Enhancement

TARRONI, GIACOMO;CORSI, CRISTIANA;VERONESI, FEDERICO;LAMBERTI, CLAUDIO;
2012

Abstract

We present an automated technique using noise-based level-set methods and non-rigid registration for myocardial detection as a basis for perfusion quantification from cardiac magnetic resonance (CMR) images. We studied 27 patients undergoing contrast-enhanced CMR imaging (1.5T) at rest and during adenosine stress, and validated our technique against conventional manual analysis both directly and using quantitative coronary angiography (QCA) as reference. Contrast enhancement time-curves were constructed and used to calculate a number of perfusion indices. Measured segmental pixel intensities in each frame correlated highly with manual analysis (r=0.95). Bland-Altman analysis showed small biases (1.3 at rest; 0.0 at stress) and narrow limits of agreement (±13 at rest; ±14 at stress). The derived perfusion indices showed the same diagnostic accuracy as manual analysis (AUC up to 0.71 vs. 0.70). These results indicate that our automated technique allows fast detection of myocardial ROIs and quantification of perfusion abnormalities as accurately as manual analysis.
2012
Atti del terzo Congresso Nazionale di Bioingegneria
278
279
G. Tarroni, C. Corsi, P.F. Antkowiak, F. Veronesi, C.M. Kramer, F.H. Epstein, et al. (2012). Automated Segmentation and Non-rigid Registration for MRI-Based Quantification of Myocardial First-Pass Contrast Enhancement. BOLOGNA : Patron.
G. Tarroni; C. Corsi; P.F. Antkowiak; F. Veronesi; C.M. Kramer; F.H. Epstein; C. Lamberti; A.R. Patel; V. Mor-Avi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/130684
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