Author manuscript, published in "European Society for Magnetic Resonance in Medicine and Biology (ESMRMB), Lisbonne:

45432 Automatic evaluation of the peri-infarct area of myocardial infarction from delayed enhancement MRI Category: Scientific Session Communications Topic: Preclinical Studies and Basic Science / Processing and quantification: imaging Authors: A. Lalande , V. Valindria, M. Angue, N. Vignon, A. Cochet , F. Brunotte ; Dijon,FR,University Hospital of Dijon,Department of MR Spectroscopy, Dijon,FR,University of Burgundy,LE2I (UMR CNRS 6306) Purpose / Introduction Delay Enhancement Magnetic Resonance Imaging (DE-MRI) can be considered as the gold standard for the assessment of myocardial viability after myocardial infarction (MI). Around the infarcted areas that appear with hyperenhanced signal, there is a peri-infarct border zone that may be an important arrhythmogenic substrate. The extent of this area is an independent predictor of post-MI mortality (1). However, it is difficult to separate peri-infarct border zones from infarct core or normal areas, because of their intermediate signal intensity. We propose a new automatic approach to detect this area, based on a modified Gaussian Mixture Model (GMM) and spatial-weighted fuzzy clustering. Subjects and Methods DE-MRI images were acquired with PSIR sequence 10 minutes after injection of a gadolinium-based contrast agent on 20 patients with acute MI. A series of short axis slices cover the left ventricle. Myocardial borders were manually drawn. The myocardium area was segmented as normal and pathologic areas via a GMM. By considering all images, the false positive pixels are eliminated thanks to 3D connectivity analysis and feature analysis. Using assumptions that pixels of the peri-infarct area have a signal intensity intermediate between remote myocardium and infarct core, and that their location is surrounding the infarct core, spatial fuzzy clustering allows their detection, modifying the initial GMM (Figure 1). No reflow was defined as a dark area surrounded by MI area. The automatic segmentation was visually evaluated by three independent observers, by classifying the quality of the segmentation from 1 (very poor segmentation) to 5 (perfect).