Multiframe temporal estimation of cardiac nonrigid motion

Abstract

A robust, flexible system for tracking the point to point nonrigid motion of the left ventricular (LV) endocardial wall in image sequences has been developed. This system is unique in its ability to model motion trajectories across multiple frames. The foundation of this system is an adaptive transversal filter based on the recursive least-squares algorithm. This filter facilitates the integration of models for periodicity and proximal smoothness as appropriate using a contour-based description of the object's boundaries. A set of correspondences between contours and an associated set of correspondence quality measures comprise the input to the system. Frame-to-frame relationships from two different frames of reference are derived and analyzed using synthetic and actual images. Two multiframe temporal models, both based on a sum of sinusoids, are derived. Illustrative examples of the system's output are presented for quantitative analysis. Validation of the system is performed by comparing computed trajectory estimates with the trajectories of physical markers implanted in the LV wall. Sample case studies of marker trajectory comparisons are presented. Ensemble statistics from comparisons with 15 marker trajectories are acquired and analyzed. A multiframe temporal model without spatial periodicity constraints was determined to provide excellent performance with the least computational cost. A multiframe spatiotemporal model provided the best performance based on statistical standard deviation, although at significant computational expense.

Consistent Estimation of Cardiac Motions by 4D Image Registration

“…The second category of methods uses segmentation of the myocardial wall, followed by geometrical and mechanical modeling using active contours or surfaces to extract the displacement field and to perform the motion analysis [5,7,8]. For matching two contours or surfaces, curvatures are frequently used to establish initial sparse correspondences, followed by the dense correspondence interpolation in other myocardial positions by regularization or mechanical modeling [5,9]. The third category of methods uses energy-based warping or optical flow techniques to compute the displacement of the myocardium [10][11][12].…”

Cardiac Motion Analysis from Ultrasound Sequences Using Non-rigid Registration

“…The sequence is generated by warping an end-diastole apical view image using cubic spline interpolation (2). We used the following motion model…”

“…The deformation fields, as well as derived parameters such as myocardial strain, can be found with good accuracy [1,2,3].…”

“…Some authors introduce temporal modelling of the cardiac motion to provide temporal smoothness and better motion tracking [1,2]. Alternative methods use energy-based warping and optical flow techniques to compute the displacement of the myocardium [5,6,7].…”

Finite Deformation Guided Nonlinear Filtering for Multiframe Cardiac Motion Analysis

“…In addition, multiframe analysis is also of paramount importance for cardiac motion recovery. The temporal kinematics coherence plays key roles in achieving robust motion estimates, especially when there are uncertainties in system models and noises in input data [7,8,10]. Unfortunately, none of these multiframe works have employed the proper anisotropic and finite deformation constraints.…”