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Time-to-peak measurements and single-parameter observations are cumbersome and often confusing for quantifying local changes in myocardial function. Recent spatiotemporal normalization techniques can provide a global picture of myocardial motion and strain patterns and overcome some of these limitations. Despite these advances, the quantification of pattern changes remains descriptive, which limits their relevance for longitudinal studies. Our paper provides a new perspective to the longitudinal analysis of myocardial motion. Non-rigid registration (diffeomorphic registration via currents) is used to match pairs of patterns, and pattern changes are inferred from the registration output. Scalability is added to the different components of the input patterns in order to tune up the contributions of the spatial, temporal and magnitude dimensions to data changes, which are of interest for our application. The technique is illustrated on 2D echocardiographic sequences from 15 patients with hypertrophic obstructive cardiomyopathy. These patients underwent biventricular pacing, which aims at provoking mechanical dyssynchrony to reduce left ventricular outflow tract (LVOT) obstruction. We demonstrate that our method can automatically quantify timing and magnitude changes in myocardial motion between baseline (non-paced) and 1 year follow-up (pacing on), resulting in a more robust analysis of complex patterns and subtle changes. Our method helps confirming that the reduction of LVOT pressure gradient actually comes from the induction of the type of dyssynchrony that was expected., (Copyright © 2014 Elsevier B.V. All rights reserved.)

In this paper, we present a new method for the automatic comparison of myocardial motion patterns and the characterization of their degree of abnormality, based on a statistical atlas of motion built from a reference healthy population. Our main contribution is the computation of atlas-based indexes that quantify the abnormality in the motion of a given subject against a reference population, at every location in time and space. The critical computational cost inherent to the construction of an atlas is highly reduced by the definition of myocardial velocities under a small displacements hypothesis. The indexes we propose are of notable interest for the assessment of anomalies in cardiac mobility and synchronicity when applied, for instance, to candidate selection for cardiac resynchronization therapy (CRT). We built an atlas of normality using 2D ultrasound cardiac sequences from 21 healthy volunteers, to which we compared 14 CRT candidates with left ventricular dyssynchrony (LVDYS). We illustrate the potential of our approach in characterizing septal flash, a specific motion pattern related to LVDYS and recently introduced as a very good predictor of response to CRT., (Copyright © 2010 Elsevier B.V. All rights reserved.)

Objective: To investigate the presence of any regional myocardial deformation abnormalities in Marfan syndrome (MFS) and determine the benefits of using advanced echocardiography compared to conventional techniques., Background: Myocardial dysfunction in MFS may be caused by extracellular matrix remodeling thus, resulting in uniform reduced functionality. However, increased aortic stiffness may cause segmental ventricular abnormalities. Strain rate imaging (SRI) constitutes a validated technique to assess regional deformation in various clinical conditions. With this in mind, we aimed to investigate biventricular function in MFS using SRI., Methods: Forty-four MFS patients (mean age 30 ± 12 years, 26 men) and 49 controls without valvular disease were examined using SRI. Ejection fraction (EF) was calculated by the Simpson's biplane method. Biventricular deformation was assessed by measuring strain/strain rate. Strain values were divided by left ventricular (LV) end-diastolic volume to adjust LV deformation for geometry changes providing a strain index (SI). Aortic stiffness was evaluated using the β-stiffness index., Results: EF (%) was reduced in MFS patients (59 ± 5 vs 72 ± 4, P < 0.001), whereas β-stiffness was increased (P < 0.001). LV radial and LV and right ventricular (RV) long-axis strain values (%) were reduced in the patient group (70 ± 17 vs 93 ± 10; 19 ± 2 vs 25 ± 2; 30 ± 9 vs 36 ± 8, respectively, P < 0.001). Strain rate measurements were also reduced (P < 0.001). In a multiple regression analysis, MFS diagnosis was negatively associated with LV SI (-0.262 [-0.306, -0.219], P < 0.001). β-Stiffness was negatively associated with SI obtained from the septum, inferior and anterior walls. ROC analyses demonstrated that SRI, when compared with conventional echocardiography, had higher sensitivity and specificity in predicting biventricular dysfunction in MFS., Conclusions: Our study showed a uniform reduction in biventricular deformation in MFS. These findings suggest that assessment of myocardial function using advanced echocardiographic techniques could be more accurate in MFS patient evaluation than conventional echocardiography alone., (© 2011, Wiley Periodicals, Inc.)

Aims: Previous studies provided evidence about left ventricular systolic and diastolic dysfunction in adults with Marfan syndrome (MFS). However, in the literature, data on right ventricular and bi-atrial diastolic function are limited. We aimed to investigate whether, in the absence of significant valvular disease, diastolic dysfunction is present not only in both ventricles but also in the atrial cavities., Methods and Results: Seventy-two adult unoperated MFS patients and 73 controls without significant differences in age, sex, and body surface area from the patient group were studied using two-dimensional, pulsed, and colour-Doppler and tissue-Doppler imaging (TDI). Biventricular early filling measurements were significantly decreased in MFS patients when compared with controls (P < 0.001). Pulsed TDI early filling measurements obtained from five mitral annular regions and over the lateral tricuspid valve corner were significantly reduced in the patient group (P < 0.001). Indices reflecting atrial function at the reservoir, conduit and contractile phases were also significantly decreased in MFS patients (P < 0.001)., Conclusion: This study demonstrated significant biventricular diastolic and biatrial systolic and diastolic dysfunction in MFS patients. Our findings suggest that MFS affects diastolic function independently. Diastolic abnormalities could be attributed to fibrillin-1 deficiency and dysregulation of transforming growth factor-beta activity in the cardiac extracellular matrix.

In this paper, we propose a complete framework for the automatic detection and quantification of abnormal heart motion patterns using Statistical Atlases of Motion built from healthy populations. The method is illustrated on CRT patients with identified cardiac dyssynchrony and abnormal septal motion on 2D ultrasound (US) sequences. The use of the 2D US modality guarantees that the temporal resolution of the image sequences is high enough to work under a small displacements hypothesis. Under this assumption, the computed displacement fields can be directly considered as cardiac velocities. Comparison of subjects acquired with different spatiotemporal resolutions implies the reorientation and temporal normalization of velocity fields in a common space of coordinates. Statistics are then performed on the reoriented vector fields. Results show the ability of the method to correctly detect abnormal motion patterns and quantify their distance to normality. The use of local p-values for quantifying abnormal motion patterns is believed to be a promising strategy for computing new markers of cardiac dyssynchrony for better characterizing CRT candidates.

Spectral estimation is a major component in studies aiming at characterizing biological tissues through the analysis of backscattered radio frequency (RF) ultrasonic signals and images. However, conventional spectral estimation techniques yield a well-known trade-off between spatial resolution and variance. The backscattered signals are stochastic by nature, so short-term local analysis results in a high variance of the estimates, which cannot efficiently be reduced through conventional spatial averaging. We address this issue by describing a spectral estimation technique that reduces the variance of the estimates (by smoothing the local estimates in spectrally homogeneous regions) while preserving spectral discontinuities (i.e., the smoothing is not performed across regions with different spectral contents). The proposed approach is set in a Bayesian framework and is based on local autoregressive (AR) estimation, constrained by smoothness priors. These smoothness priors are introduced through a Markov random field in which the associated potential functions are nonquadratic, allowing thereby to preserve discontinuity. The method is validated on simulated RF images and tested on echocardiographic images acquired in vivo. The results are compared to the estimates provided by the conventional Burg technique. These results clearly demonstrate the ability of the proposed approach to improve spectral estimation in terms of variance reduction and discontinuity detection.