A novel subspace approximation technique using B-spline properties significantly improved computational time for estimating dense displacement fields from cardiac tagged MR images.
A novel subspace approximation technique using B-splines significantly reduces computational time for estimating left ventricular motion from MR tagged images.
Cardiac motion estimation is very important in understanding cardiac dynamics and in noninvasive diagnosis of heart disease. Magnetic resonance (MR) imaging tagging is a technique for measuring heart deformations. In cardiac tagged MR images, a set of dark lines are noninvasively encoded within myocardial tissue providing the means for measurement of deformations of the heart. The points along tag lines measured in different frames and in different directions carry important information for determining the three-dimensional nonrigid movement of left ventricle. However, these measurements are sparse and, therefore, multidimensional interpolation techniques are needed to reconstruct a dense displacement field. In this paper, a novel subspace approximation technique is used to accomplish this task. We formulate the displacement estimation as a variational problem and then project the solution into spline subspaces. Efficient numerical methods are derived by taking advantages of B-spline properties. The proposed technique significantly improves our previous results reported in 3 with respect to computational time. The method is applied to a temporal sequence of two-dimensional images and is validated with simulated and in vivo heart data.
Wang et al. (Fri,) conducted a other in Cardiac motion estimation. Subspace approximation technique using B-splines vs. Previous methods was evaluated on Computational time. A novel subspace approximation technique using B-spline properties significantly improved computational time for estimating dense displacement fields from cardiac tagged MR images.