Muthuvel Arigovindan

Myocardial motion analysis from B-mode echocardiograms

The quantitative assessment of cardiac motion is a fundamental concept to evaluate ventricular malfunction. We present a new optical-flow-based method for estimating heart motion from two-dimensional echocardiographic sequences. To account for typical heart motions, such as contraction/expansion and shear, we analyze the images locally by using a local-affine model for the velocity in space and a linear model in time. The regional motion parameters are estimated in the least-squares sense inside a sliding spatiotemporal B-spline window. Robustness and spatial adaptability is achieved by estimating the model parameters at multiple scales within a coarse-to-fine multiresolution framework. We use a wavelet-like algorithm for computing B-spline-weighted inner products and moments at dyadic scales to increase computational efficiency. In order to characterize myocardial contractility and to simplify the detection of myocardial dysfunction, the radial component of the velocity with respect to a reference point is color coded and visualized inside a time-varying region of interest. The algorithm was first validated on synthetic data sets that simulate a beating heart with a speckle-like appearance of echocardiograms. The ability to estimate motion from real ultrasound sequences was demonstrated by a rotating phantom experiment. The method was also applied to a set of in vivo echocardiograms from an animal study. Motion estimation results were in good agreement with the expert echocardiographic reading.

Variational image reconstruction from arbitrarily spaced samples: a fast multiresolution spline solution

We propose a novel method for image reconstruction from nonuniform samples with no constraints on their locations. We adopt a variational approach where the reconstruction is formulated as the minimizer of a cost that is a weighted sum of two terms: 1) the sum of squared errors at the specified points and 2) a quadratic functional that penalizes the lack of smoothness. We search for a solution that is a uniform spline and show how it can be determined by solving a large, sparse system of linear equations. We interpret the solution of our approach as an approximation of the analytical solution that involves radial basis functions and demonstrate the computational advantages of our approach. Using the two-scale relation for B-splines, we derive an algebraic relation that links together the linear systems of equations specifying reconstructions at different levels of resolution. We use this relation to develop a fast multigrid algorithm. We demonstrate the effectiveness of our approach on some image reconstruction examples.

Full Motion and Flow Field Recovery From Echo Doppler Data

We present a new computational method for reconstructing a vector velocity field from scattered, pulsed-wave ultrasound Doppler data. The main difficulty is that the Doppler measurements are incomplete, for they do only capture the velocity component along the beam direction. We thus propose to combine measurements from different beam directions. However, this is not yet sufficient to make the problem well posed because 1) the angle between the directions is typically small and 2) the data is noisy and nonuniformly sampled. We propose to solve this reconstruction problem in the continuous domain using regularization. The reconstruction is formulated as the minimizer of a cost that is a weighted sum of two terms: 1) the sum of squared difference between the Doppler data and the projected velocities 2) a quadratic regularization functional that imposes some smoothness on the velocity field. We express our solution for this minimization problem in a B-spline basis, obtaining a sparse system of equations that can be solved efficiently. Using synthetic phantom data, we demonstrate the significance of tuning the regularization according to the a priori knowledge about the physical property of the motion. Next, we validate our method using real phantom data for which the ground truth is known. We then present reconstruction results obtained from clinical data that originate from 1) blood flow in carotid bifurcation and 2) cardiac wall motion

Multiresolution moment filters: theory and applications

We introduce local weighted geometric moments that are computed from an image within a sliding window at multiple scales. When the window function satisfies a two-scale relation, we prove that lower order moments can be computed efficiently at dyadic scales by using a multiresolution wavelet-like algorithm. We show that B-splines are well-suited window functions because, in addition to being refinable, they are positive, symmetric, separable, and very nearly isotropic (Gaussian shape). We present three applications of these multiscale local moments. The first is a feature-extraction method for detecting and characterizing elongated structures in images. The second is a noise-reduction method which can be viewed as a multiscale extension of Savitzky-Golay filtering. The third is a multiscale optical-flow algorithm that uses a local affine model for the motion field, extending the Lucas-Kanade optical-flow method. The results obtained in all cases are promising.

Multiscale Motion Mapping—A Novel Computer Vision Technique for Quantitative, Objective Echocardiographic Motion Measurement Independent of Doppler: First Clinical Description and Validation

Background—Objective, quantitative, segmental noninvasive/bedside measurement of cardiac motion is highly desirable in cardiovascular medicine, but current technology suffers from significant drawbacks, such as subjectivity of conventional echocardiographic reading, angle dependence of tissue Doppler measurements, radiation exposure by computer tomography, and infrastructure requirements in MRI. We hypothesized that computer vision technology could represent a powerful new paradigm for quantification in echocardiography. Methods and Results—We present multiscale motion mapping, a novel computer vision technology that is based on mathematical image processing and that exploits echocardiographic information in a fashion similar to the human visual system. It allows Doppler- and border-independent determination of motion and deformation in echocardiograms at arbitrary locations. Correctness of the measurements was documented in synthetic echocardiograms and phantom experiments. Exploratory case studies demonstrated its usefulness in a series of complex motion analyses that included abnormal septal motion and analysis of myocardial twisting. Clinical applicability was shown in a consecutive series of echocardiograms, in which good feasibility, good correlation with expert rating, and good intraobserver and interobserver concordance were documented. Separate assessment of 2D displacement and deformation at the same location was successfully applied to elucidate paradoxical septal motion, a common clinical problem. Conclusions—This is the first clinical report of multiscale motion mapping, a novel approach to echocardiographic motion quantification. For the first time, full 2D echocardiographic assessment of both motion and deformation is shown to be feasible. Overcoming current limitations, this computer vision–based technique opens a new door to objective analysis of complex heart motion.

Multigrid image reconstruction from arbitrarily spaced samples

We propose a novel multiresolution-multigrid based signal reconstruction method from arbitrarily spaced samples. The signal is reconstructed on a uniform grid using B-splines basis functions. The computation of spline weights is formulated as a variational problem. Specifically, we minimize a cost that is a weighted sum of two terms: (i) the sum of squared errors at the specified points; (ii) a quadratic functional that penalizes the lack of smoothness. The problem is equivalent to solving a very large system of linear equations, with the dimension equal to the number of grid points. We develop a computationally efficient multiresolution-multigrid scheme for solving the system. We demonstrate the method with image reconstruction from contour points.

Motion analysis of echocardiograms using a local-affine, spatio-temporal model

We present a new method for estimating heart motion from two-dimensional (2D) echocardiographic sequences. It is inspired by the Lucas-Kanade algorithm for optical flow which estimates motion parameters over a sliding window. However, instead of assuming that the motion is constant within the analysis window, we consider a model that is locally affine and can account for typical heart motions such as dilation/contraction and shear. Another refinement is spatial adaptivity which is achieved by estimating displacement vectors at multiple scales and selecting the most promising fit. The affine parameters are estimated in the least squares sense using a separable spatial (resp., spatio-temporal) B-spline window. This particular choice is motivated by the fact that the B-splines are nearly isotropic (Gaussian-like) and that they satisfy a two-scale equation. We use this latter property to derive a wavelet-like algorithm that leads to a fast computation of B-spline-weighted inner products and moments at dyadic scales, which speeds up our method considerably. We test the algorithm on synthetic and real ultrasound sequences and show that it compares favorably with other methods, such as Lucas-Kanade and Horn-Schunk.

Bimodal myocardial motion analysis from B-mode and tissue Doppler ultrasound

We present a new method for estimating heart motion from two-dimensional echocardiographic sequences by exploiting two ultrasound modalities: B-mode and tissue Doppler. The algorithm estimates a two-dimensional velocity field locally by using a spatial affine velocity model inside a sliding window. Within each window, we minimize a local cost function that is composed of two quadratic terms: an optical flow constraint that involves the B-mode data and a constraint that enforces the agreement of the velocity field with the directional tissue Doppler measurements. The relative influence of the two different modalities to the resulting solution is controlled by an adjustable weighting parameter. Robustness is achieved by a coarse-to-fine multi-scale approach.

Myocardial Motion Analysis and Visualization from Echocardiograms

We present a new framework to estimate and visualize heart motion from echocardiograms. For velocity estimation, we have developed a novel multiresolution optical flow algorithm. In order to account for typical heart motions like contraction/expansion and shear, we use a local affine model for the velocity in space and time. The motion parameters are estimated in the least-squares sense inside a sliding spatio-temporal window. The estimated velocity field is used to track a region of interest which is represented by spline curves. In each frame, a set of sample points on the curves is displaced according to the estimated motion field. The contour in the subsequent frame is obtained by a least-squares spline fit to the displaced sample points. This ensures robustness of the contour tracking. From the estimated velocity, we compute a radial velocity field with respect to a reference point. Inside the time-varying region of interest, the radial velocity is color-coded and superimposed on the original image sequence in a semi-transparent fashion. In contrast to conventional Tissue Doppler methods, this approach is independent of the incident angle of the ultrasound beam. The motion analysis and visualization provides an objective and robust method for the detection and quantification of myocardial malfunctioning. Promising results are obtained from synthetic and clinical echocardiographic sequences.

Myocardial motion and strain rate analysis from ultrasound sequences

We present an optical flow-based algorithm to estimate heart wall motion from ultrasound sequences. The method exploits two ultrasound modalities, i.e., B-mode (grayscale data) and tissue Doppler (partial velocity measurements). We use a local affine velocity model to account for typical heart motions such as contraction/expansion and shear. The affine model parameters give also access to so-called strain rate parameters that describe local myocardial deformation such as wall thickening. The estimation of large motions is made possible through the use of a coarse-to-fine multi-scale strategy, which also adds robustness to the method.