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.
Automated Segmentation and Non-rigid Registration for MRI-Based Quantification of Myocardial First-Pass Contrast Enhancement / G. Tarroni; C. Corsi; P.F. Antkowiak; F. Veronesi; C.M. Kramer; F.H. Epstein; C. Lamberti; A.R. Patel; V. Mor-Avi. - STAMPA. - (2012), pp. 278-279. (Intervento presentato al convegno terzo Congresso Nazionale di Bioingegneria tenutosi a Università Roma Tre, Roma, Italy nel 26-29 giugno 2012).
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
