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


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.
Comte
Alexandre
A
Laboratoire de Biophysique, Faculté de Médecine, Université de Bourgogne, Dijon, France. alexandre.comte@u-bourgogne.fr
Lalande
Alain
A
Cochet
Alexandre
A
Walker
Paul M
PM
Wolf
Jean-Eric
JE
Cottin
Yves
Y
Brunotte
François
F
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003287:Contrast Media; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D019028:Magnetic Resonance Imaging, Cine; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000473:pathology*; D015425:Myocardial Reperfusion; D011237:Predictive Value of Tests; D011379:Prognosis; D011446:Prospective Studies; D018709:Statistics, Nonparametric
D003287:Contrast Media