Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation

Abstract

Abstract-We propose a new spatio-temporal elastic registration algorithm for motion reconstruction from a series of images. The specific application is to estimate displacement fields from two-dimensional ultrasound sequences of the heart. The basic idea is to find a spatio-temporal deformation field that effectively compensates for the motion by minimizing a difference with respect to a reference frame. The key feature of our method is the use of a semi-local spatio-temporal parametric model for the deformation using splines, and the reformulation of the registration task as a global optimization problem. The scale of the spline model controls the smoothness of the displacement field. Our algorithm uses a multiresolution optimization strategy to obtain a higher speed and robustness.We evaluated the accuracy of our algorithm using a synthetic sequence generated with an ultrasound simulation package, together with a realistic cardiac motion model. We compared our new global multiframe approach with a previous method based on pairwise registration of consecutive frames to demonstrate the benefits of introducing temporal consistency. Finally, we applied the algorithm to the regional analysis of the left ventricle. Displacement and strain parameters were evaluated showing significant differences between the normal and pathological segments, thereby illustrating the clinical applicability of our method.

Septal Flash Assessment on CRT Candidates Based on Statistical Atlases of Motion

“…This transformation aims at correcting probe motion during the acquisition, and adds robustness towards out-of-plane motion, as the assumption ϕ Tj ,Tj+1 = Id generally made in other works [4] is not verified in our database of 2D US sequences.…”

“…Recent works used non-rigid registration techniques to build Statistical Atlases of Motion of the heart from magnetic resonance image sequences [3], in which the displacement fields reflect the movement of anatomical structures. The use of 4D transformation models was presented in [4] for motion tracking over sequences of images. Registration is performed between frames at time points t i and t 0 (i = 0).…”

Organ-focused mutual information for nonrigid multimodal registration of liver CT and Gd–EOB–DTPA-enhanced MRI

“…The transformation model is defined as a linear combination of B-spline basis functions located on a uniform grid. B-spline functions have been widely used to represent deformations (Kybic and Unser, 2003;Ledesma-Carbayo et al, 2005;Oguro et al, 2009;Rueckert et al, 1999;Schnabel et al, 2001), motivated by their compact support, computational simplicity, good approximation properties, and implicit smoothness. We also use B-spline functions for representing continuous images derived from a set of samples (Kybic and Unser, 2003;Thévenaz and Unser, 2000).…”

Letter to the Editor Regarding Paper “Automatic Computation of Left Ventricular Volume Changes over a Cardiac Cycle from Echocardiography Images by Nonlinear Dimensionality Reduction”

“…The main tool in this method is the function "f" which is an isometric map [2,3]. I will be interested to know the structure/ formula or relationship of this isometric function which most probably contains a lot of practical information of the left ventricular function and structure.…”