Regional heart motion abnormality detection via multiview fusion.

This study investigates regional heart motion abnormality detection via multiview fusion in cine cardiac MR images. In contrast to previous methods which rely only on short-axis image sequences, the proposed approach exploits the information from several other long-axis image sequences, namely, 2-chamber, 3-chamber and 4-chamber MR images. Our analysis follows the standard issued by American Heart Association to identify 17 standardized left ventricular segments. The proposed method first computes an initial sequence of corresponding myocardial points using a nonrigid image registration algorithm within each sequence. Then, these points were mapped to 3D space and tracked using Unscented Kalman Filter (UKS). We propose a maximum likelihood based track-to-track fusion approach to combine UKS tracks from multiple image views. Finally, we use a Shannon's differential entropy of distributions of potential classifiers obtained from multiview fusion estimates, and a naive Bayes classifier algorithm to automatically detect abnormal functional regions of the myocardium. We proved the benefits of the proposed method by comparing the classification results with and without fusion over 480 regional myocardial segments obtained from 30 subjects. The evaluations in comparisons to the ground truth classifications by radiologists showed that the proposed fusion yielded an area-under-the-curve (AUC) of 95.9%, bringing a significant improvement of 3.8% in comparisons to previous methods that use only short-axis images.