Automatic fuzzy classification of the washout curves from magnetic resonance first-pass perfusion Imaging after myocardial infarction

Abstract : Abstract: Objectives: We sought to investigate the diagnostic ability of cardiac magnetic resonance imaging (MRI) perfusion in acute reper-fused 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.
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https://hal.archives-ouvertes.fr/hal-00788189
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Submitted on : Thursday, February 14, 2013 - 8:23:16 AM
Last modification on : Wednesday, September 5, 2018 - 5:04:04 PM

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Alexandre Comte, Alain Lalande, Alexandre Cochet, Paul Walker, Jean-Eric Wolf, et al.. Automatic fuzzy classification of the washout curves from magnetic resonance first-pass perfusion Imaging after myocardial infarction. Investigative Radiology, Lippincott, Williams & Wilkins, 2005, 40 (8), pp.545-555. ⟨10.1097/01.rli.0000170448.31487.1b⟩. ⟨hal-00788189⟩

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