Atlas-Based Quantification of Myocardial Motion Abnormalities: Added-Value for Understanding the Effect of Cardiac Resynchronization Therapy

Duchateau, Nicolás; Doltra, Adelina; Craene, Mathieu De; Piella, Gemma; Castel, María Ángeles; Mont, Lluı́s; Brugada, Josep; Frangi, Alejandro F.; Sitges, Marta

doi:10.1016/j.ultrasmedbio.2012.08.009

Cited by 7 publications

(5 citation statements)

References 47 publications

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“…This comes from the fact that for this subject, instants for AVC are rather different between baseline and follow-up (39% vs. 50% of the nonnormalized cardiac cycle, respectively). Complementary illustrations, showing that important characteristics of the patterns may be affected by the lack of temporal normalization, can be found in our earlier work (Duchateau et al, 2012c(Duchateau et al, , 2014.…”

Section: Contribution Of the Normalization Stepsmentioning

confidence: 60%

Quantification of local changes in myocardial motion by diffeomorphic registration via currents: Application to paced hypertrophic obstructive cardiomyopathy in 2D echocardiographic sequences

Duchateau¹,

Giraldeau²,

Gabrielli³

et al. 2015

Medical Image Analysis

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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.

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Section: Contribution Of the Normalization Stepsmentioning

confidence: 60%

Quantification of local changes in myocardial motion by diffeomorphic registration via currents: Application to paced hypertrophic obstructive cardiomyopathy in 2D echocardiographic sequences

Duchateau¹,

Giraldeau²,

Gabrielli³

et al. 2015

Medical Image Analysis

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“…Furthermore, compared with the analysis of static geometries alone, time-varying geometric models can provide more comprehensive information on changes in cardiac structure and function which enables the analysis of haemodynamics 7 , or to determine myocardial strain rate 8 . Similarly, motion atlases, comprising spatiotemporal information, have been applied to effectively detect dilated cardiomyopathy 9 , as well as to predict responses to cardiac resynchronisation therapy 10 , 11 .…”

Section: Introductionmentioning

confidence: 99%

Correcting bias in cardiac geometries derived from multimodal images using spatiotemporal mapping

Zhao

Mauger

Gilbert

et al. 2023

Sci Rep

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Cardiovascular imaging studies provide a multitude of structural and functional data to better understand disease mechanisms. While pooling data across studies enables more powerful and broader applications, performing quantitative comparisons across datasets with varying acquisition or analysis methods is problematic due to inherent measurement biases specific to each protocol. We show how dynamic time warping and partial least squares regression can be applied to effectively map between left ventricular geometries derived from different imaging modalities and analysis protocols to account for such differences. To demonstrate this method, paired real-time 3D echocardiography (3DE) and cardiac magnetic resonance (CMR) sequences from 138 subjects were used to construct a mapping function between the two modalities to correct for biases in left ventricular clinical cardiac indices, as well as regional shape. Leave-one-out cross-validation revealed a significant reduction in mean bias, narrower limits of agreement, and higher intraclass correlation coefficients for all functional indices between CMR and 3DE geometries after spatiotemporal mapping. Meanwhile, average root mean squared errors between surface coordinates of 3DE and CMR geometries across the cardiac cycle decreased from 7 ± 1 to 4 ± 1 mm for the total study population. Our generalised method for mapping between time-varying cardiac geometries obtained using different acquisition and analysis protocols enables the pooling of data between modalities and the potential for smaller studies to leverage large population databases for quantitative comparisons.

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“…This measure corresponds to the distance to the average pattern normalized by the pattern variability among operators. To better analyze the results, we computed the p-value associated to this distance, under the assumption that the distribution of strain values (at each point of myocardium and each instant of the cardiac cycle, among the pool of operators) is Gaussian, as done in our anterior works (Duchateau et al 2011, Duchateau et al 2012. Additionally, we estimated the maximum of the 2D normalized crosscorrelation between the strain pattern obtained by each operator and the average strain pattern from the rest of operators, values of 0 and 1 meaning no correlation and perfect correlation, respectively.…”

Section: Statistical Analysis Of Myocardial Delineations and Strain Pmentioning

confidence: 99%

Variability in the Assessment of Myocardial Strain Patterns: Implications for Adequate Interpretation

Duchateau

Lončarić

Čikeš

et al. 2020

Ultrasound in Medicine & Biology

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Variability in global and regional peak strain has been thoroughly studied, but not the variability in the spatiotemporal myocardial strain patterns. This study reports on such variability and its implications for adequate disease interpretation. Forty in-training operators were distributed on 20 workstations, and analyzed six cases with representative deformation patterns with commercial speckle-tracking. Inter-operator differences were quantified through the variability in myocardial delineations, spatiotemporal longitudinal strain patterns, and peak longitudinal strain. Intra-operator differences were assessed similarly using 10 repeated measurements from a single clinician expert. Delineations varied mainly along the lateral wall and at the valve level. Peak longitudinal strain variability was low to moderate. The spatiotemporal strain patterns were consistent despite high variability at the apex and near the valve. The results indicate that relevant pattern assessment is possible despite heterogeneous experience with speckle-tracking, and that careful interpretation of pattern abnormalities should be recommended before a more systematic quantitative analysis.

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Atlas-Based Quantification of Myocardial Motion Abnormalities: Added-Value for Understanding the Effect of Cardiac Resynchronization Therapy

Cited by 7 publications

References 47 publications

Quantification of local changes in myocardial motion by diffeomorphic registration via currents: Application to paced hypertrophic obstructive cardiomyopathy in 2D echocardiographic sequences

Quantification of local changes in myocardial motion by diffeomorphic registration via currents: Application to paced hypertrophic obstructive cardiomyopathy in 2D echocardiographic sequences

Correcting bias in cardiac geometries derived from multimodal images using spatiotemporal mapping

Variability in the Assessment of Myocardial Strain Patterns: Implications for Adequate Interpretation

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