A graph TV minimization framework for cardiac motion analysis

Abstract

Myocardial ischemia or coronary artery disease can be identified and located by analyzing the movement and deformation of the heart. Therefore, to accurately and non-invasively diagnose the location and extent of ischemic or infarcted myocardium, it is of great practical significance to quantitatively determine the motion/deformation parameters of myocardial tissue. In this paper, the myocardial material parameters are used as a priori information and combined with a continuum mechanics model to restore the cardiac cycle motion under the spatial constraints of the graph total variation (GTV). In the motion reconstruction, the biomechanical model establishes the relationship between stress and deformation through system dynamics. The total variation of the graph proposed in this paper ignores the spatial distance, establishes the connection between similar regions in the image, overcomes the limitation of considering only the similarity with adjacent regions, and preserves the texture details and fine structure. Because GTV uses the K-nearest neighbor algorithm (KNN) to classify regional similarity, the connection between similar regions is stronger, therein achieving computational scalability and lower computational complexity. The accuracy of the strategy with and promising application results from synthetic data, magnetic resonance (MR) phase contrast, and gradient echo cine MR image sequence are demonstrated.