Flow Network Tracking for Spatiotemporal and Periodic Point Matching: Applied to Cardiac Motion Analysis

TL;DR

This paper introduces a flow network-based point matching method for tracking myocardial tissue throughout the entire cardiac cycle, improving the accuracy of LV displacement and strain estimation in cardiac motion analysis.

Contribution

It presents a novel flow network model with constraints that ensure one-to-one and cyclic motion correspondence over the full cardiac cycle, enhancing tracking accuracy.

Findings

Achieved excellent tracking accuracy on synthetic data.

Observed good correlation with crystal-based strains in vivo.

Validated on both synthetic and canine echocardiographic data.

Abstract

The accurate quantification of left ventricular (LV) deformation/strain shows significant promise for quantitatively assessing cardiac function for use in diagnosis and therapy planning (Jasaityte et al., 2013). However, accurate estimation of the displacement of myocardial tissue and hence LV strain has been challenging due to a variety of issues, including those related to deriving tracking tokens from images and following tissue locations over the entire cardiac cycle. In this work, we propose a point matching scheme where correspondences are modeled as flow through a graphical network. Myocardial surface points are set up as nodes in the network and edges define neighborhood relationships temporally. The novelty lies in the constraints that are imposed on the matching scheme, which render the correspondences one-to-one through the entire cardiac cycle, and not just two consecutive frames. The constraints also encourage motion to be cyclic, which is an important characteristic of LV motion. We validate our method by applying it to the estimation of quantitative LV displacement and strain estimation using 8 synthetic and 8 open-chested canine 4D echocardiographic image sequences, the latter with sonomicrometric crystals implanted on the LV wall. We were able to achieve excellent tracking accuracy on the synthetic dataset and observed a good correlation with crystal-based strains on the in-vivo data.