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We have recently developed an automated technique using noise-based level-set methods and non-rigid registration for endocardial and epicardial border detection as a basis for perfusion quantification from cardiac magnetic resonance (CMR) images. The goal of the present work was to validate this technique against conventional manual analysis both directly and using quantitative coronary angiography as reference for significant disease (stenosis >;50%). We studied 27 patients undergoing contrast-enhanced CMR imaging (1.5T) at rest and during adenosine stress. 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 0.72 vs. 0.73). These results indicate that our automated technique allows fast detection of myocardial ROIs and quantification of stress-induced perfusion abnormalities as accurately as manual analysis.