title: Identifying regional cardiac abnormalities from myocardial strains using nontracking-based strain estimation and spatio-temporal tensor analysis.
abstract: Myocardial strain is a critical indicator of many cardiac diseases and dysfunctions. The goal of this paper is to extract and use the myocardial strain pattern from tagged magnetic resonance imaging (MRI) to identify and localize regional abnormal cardiac function in human subjects. In order to extract the myocardial strains from the tagged images, we developed a novel nontracking-based strain estimation method for tagged MRI. This method is based on the direct extraction of tag deformation, and therefore avoids some limitations of conventional displacement or tracking-based strain estimators. Based on the extracted spatio-temporal strain patterns, we have also developed a novel tensor-based classification framework that better conserves the spatio-temporal structure of the myocardial strain pattern than conventional vector-based classification algorithms. In addition, the tensor-based projection function keeps more of the information of the original feature space, so that abnormal tensors in the subspace can be back-projected to reveal the regional cardiac abnormality in a more physically meaningful way. We have tested our novel methods on 41 human image sequences, and achieved a classification rate of 87.80%. The regional abnormalities recovered from our algorithm agree well with the patient's pathology and clinical image interpretation, and provide a promising avenue for regional cardiac function analysis.
authors: Center for Computational Biomedicine Imaging and Modeling (CBIM), Rutgers University, Piscataway, NJ 08854, USA. zhenqian@gmail.com
authors: Zhen
authors: Z
authors: Qian
authors: Qingshan
authors: Q
authors: Liu
authors: Dimitris N
authors: DN
authors: Metaxas
authors: Leon
authors: L
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