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Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction
Author(s) -
Larroza Andrés,
LópezLereu María P.,
Monmeneu José V.,
Gavara Jose,
Chorro Francisco J.,
Bodí Vicente,
Moratal David
Publication year - 2018
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12783
Subject(s) - magnetic resonance imaging , receiver operating characteristic , medicine , myocardial infarction , artificial intelligence , support vector machine , pattern recognition (psychology) , nuclear medicine , radiology , computer science , cardiology
Purpose To investigate the ability of texture analysis to differentiate between infarcted nonviable, viable, and remote segments on cardiac cine magnetic resonance imaging ( MRI ). Methods This retrospective study included 50 patients suffering chronic myocardial infarction. The data were randomly split into training (30 patients) and testing (20 patients) sets. The left ventricular myocardium was segmented according to the 17‐segment model in both cine and late gadolinium enhancement ( LGE ) MRI . Infarcted myocardium regions were identified on LGE in short‐axis views. Nonviable segments were identified as those showing LGE ≥ 50%, and viable segments those showing 0 < LGE < 50% transmural extension. Features derived from five texture analysis methods were extracted from the segments on cine images. A support vector machine ( SVM ) classifier was trained with different combination of texture features to obtain a model that provided optimal classification performance. Results The best classification on testing set was achieved with local binary patterns features using a 2D + t approach, in which the features are computed by including information of the time dimension available in cine sequences. The best overall area under the receiver operating characteristic curve ( AUC ) were: 0.849, sensitivity of 92% to detect nonviable segments, 72% to detect viable segments, and 85% to detect remote segments. Conclusion Nonviable segments can be detected on cine MRI using texture analysis and this may be used as hypothesis for future research aiming to detect the infarcted myocardium by means of a gadolinium‐free approach.