Myocardial Perfusion Classification Using A Markov Random Field Constrained Gaussian Mixture Model

Abstract

Dynamic Contract Enhanced Magnetic Resonance (MR) Imaging (DCE-MRI) has been widely used as a non-invasive assessment approach to estimate the myocardial blood flow (MBF). The delineation of a hypo-perfused region (low MBF region) is important for understanding a patient's heart condition in clinical diagnosis. In this paper, a Markov random field constrained Gaussian mixture model (GMM-MRF) classification method is introduced to classify MBF maps using myocardial perfusion DCE-MRI data. The GMM-MRF method, trained with an ICM algorithm, makes use of spatial neighbourhood information to improve classification accuracy. The proposed method is applied to and assessed on both synthetic and clinical data, and compared with established classification methods.