Automated classification of LV regional wall motion based on spatio-temporal profiles from cardiac cine Magnetic Resonance Imaging

摘要

Assessment of the cardiac Left Ventricle (LV) wall motion is generally based on visual inspection or quantitative analysis of 2D+t sequences acquired in short-axis cardiac cine-Magnetic Resonance Imaging (MRI). Most often, cardiac dynamic is globally analyzed from two particular phases of the cardiac cycle. In this paper, we propose an automated method to classify regional wall motion in LV function based on spatio-temporal profiles and Support Vector Machines (SVM). This approach allows to obtain a binary classification between normal and abnormal motion, without the need of pre-processing and by exploiting all the images of the cardiac cycle. In each short-axis MRI slice level (basal, median, and apical), the spatio-temporal profiles are extracted from the selection of a subset of diametrical lines crossing opposites LV segments. Initialized at end-diastole phase, the profiles are concatenated with their corresponding projections into the successive temporal phases of the cardiac cycle. These profiles are associated to different types of information that derive from the image (gray levels), Fourier, Wavelet or Curvelet domains. The approach has been tested on a set of 14 abnormal and 6 healthy patients by using a leave-one-out cross validation and two kernel functions for SVM classifier. The best classification performance is yielded by using three-level db4 wavelet transform and SVM with a linear kernel. At each slice level the results provided a classification rate of 87.14% in apical level, 95.48% in median level and 93.65% in basal level.