OBJECTIVES: We sought to investigate the diagnostic ability of cardiac magnetic resonance imaging (MRI) perfusion in acute reperfused myocardial infarction. The study used fuzzy logic to automatically classify signal intensity-time curves from myocardial segments into 3 categories: normal, hypointense, and Hyperintense. MATERIALS AND METHODS: Thirty-eight patients with myocardial infarction underwent short-axis cine-MRI and contrast-enhanced MRI to provide data on wall thickening and the transmural extent of infarction. Of these, 17 had a second cardiac MRI to ascertain the functional recovery in each segment. RESULTS: The fuzzy logic based classification performs well (kappa= 0.87, P < 0.01) in comparison with a visual approach. Segments providing "hypo" curves do not recover (Delta = 0.11 SD = 0.96) whereas segments demonstrating the other curve types recover (Delta = 1 SD = 1.98 for "hyper" curves and Delta = 1.54 SD = 1.77 for "normal" curves). CONCLUSIONS: The proposed automatic signal intensity-time curve classification has a prognostic value when studying the functional recovery of pathologic segments and clearly identifies the no-reflow phenomenon known to induce poor recovery.
OBJECTIVES: We sought to investigate the diagnostic ability of cardiac magnetic resonance imaging (MRI) perfusion in acute reperfused myocardial infarction. The study used fuzzy logic to automatically classify signal intensity-time curves from myocardial segments into 3 categories: normal, hypointense, and Hyperintense. MATERIALS AND METHODS: Thirty-eight patients with myocardial infarction underwent short-axis cine-MRI and contrast-enhanced MRI to provide data on wall thickening and the transmural extent of infarction. Of these, 17 had a second cardiac MRI to ascertain the functional recovery in each segment. RESULTS: The fuzzy logic based classification performs well (kappa= 0.87, P < 0.01) in comparison with a visual approach. Segments providing "hypo" curves do not recover (Delta = 0.11 SD = 0.96) whereas segments demonstrating the other curve types recover (Delta = 1 SD = 1.98 for "hyper" curves and Delta = 1.54 SD = 1.77 for "normal" curves). CONCLUSIONS: The proposed automatic signal intensity-time curve classification has a prognostic value when studying the functional recovery of pathologic segments and clearly identifies the no-reflow phenomenon known to induce poor recovery.
Authors: L Natale; C Napolitano; A Bernardini; A Meduri; R Marano; A Lombardo; F Crea; L Bonomo Journal: Radiol Med Date: 2012-03-19 Impact factor: 3.469