Speckle Tracking in Interpolated Echocardiography to Estimate Heart Motion

Curiale, Ariel Hernán; Sánchez-Ferrero, Gonzalo Vegas; Aja‐Fernández, Santiago

doi:10.1007/978-3-642-38899-6_39

Cited by 5 publications

(14 citation statements)

References 17 publications

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“…On the other hand, some authors proposed the use of statistical models to provide a better characterization of speckle, such as Gaussian (Cohen and Dinstein, 2002), Rayleigh (Strintzis and Kokkinidis, 1997;Cohen and Dinstein, 2002;Yue et al, 2009), Gamma (Curíale et al, 2013a), bivariate Nakagami (Myronenko et al, 2009) and bivariate Generalized Gamma (Curíale et al, 2015).…”

Section: Similarity Measurementioning

confidence: 99%

“…In this model, the control points and spline functions are used to establish locally the correspondence between images. The FFD is computationally efficient and has been widely used in ST methods (Myronenko et al, 2007;Yue et al, 2009;De Craene et al, 2012;Curíale et al, 2013a;Heyde et al, 2013a;2013b;Piella et al, 2013) due to its computational efficiency Physical based models are dense and non-parametric transformations, where the displacement field is defined in each voxel (Holden, 2008) according to physical phenomena. Such is the case of elastic models (Bajcsy and Kovacic, 1989), fluid models (Beauchemin and Barron, 1995), optical flow and diffusion models (Thirion, 1998).…”

Section: Transformation Modelmentioning

confidence: 99%

“…This optimization can be done by using many methods, such the classical gradient descent or the steepest descent optimization (Myronenko et al, 2007;, the conjugate gradient (Curíale et al, 2013a), the Broyden Fletcher Goldfarb Shannon (L-BFGS-B) (Yue et al, 2009;De Craene et al, 2012;Heyde et al, 2013a;2013b;Piella et al, 2013) or the normalized convolution (Knutsson et al, 2005;Tautz et al, 2013) among others. Fluid, elastic and diffusion models that can be described in terms of partial differential equations are commonly optimized by using variational approaches Somphone et al, 2013;Curí-ale et al, 2013b;2015).…”

Section: Optimization Strategymentioning

confidence: 99%

“…Furthermore, a widely used strategy for improving the optimization, especially for large deformations, is to use a coarse-tofine refinement (Yeung et al, 1998a;1998b;Knutsson et al, 2005;Yue et al, 2009;Myronenko et al, 2009;De Craene et al, 2012;Curíale et al, 2013a;2013b;Heyde et al, 2013b;Somphone et al, 2013;Alessandrini et al, 2013;Piella et al, 2013;Tautz et al, 2013). This approach is highly recommended for the complex deformations such is the case of the heart motion.…”

Section: Optimization Strategymentioning

confidence: 99%

“…However, it is important to remark that, though some registration or tracking techniques are used as a necessary step for ST, the speckle information present in US images should be coded into the similarity measure used for tracking. For instance, instead of the straightforward block matching, some authors proposed to introduce the speckle model into a Free-Form Deformation (FFD) method (Myronenko et al, 2009;Yue et al, 2009;De Craene et al, 2012;Curíale et al, 2013a), a diffeomorphic demons scheme (Curíale et al, 2015), or to introduce the monogenic phase of the B-mode image into an optical flow approach (Knutsson et al, 2005;Alessandrini et al, 2013;Tautz et al, 2013). So, according to the proposals of the state of the art for ST, one can easily distinguish three necessary and interrelated parts within a generic ST method 1.…”

Section: Introductionmentioning

confidence: 99%

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Influence of ultrasound speckle tracking strategies for motion and strain estimation

Curiale

Vegas-Sánchez-Ferrero

Aja‐Fernández

2016

Medical Image Analysis

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Speckle Tracking is one of the most prominent techniques used to estimate the regional movement of the heart based on ultrasound acquisitions. Many different approaches have been proposed, proving their suitability to obtain quantitative and qualitative information regarding myocardial deformation, motion and function assessment. New proposals to improve the basic algorithm usually focus on one of these three steps: (1) the similarity measure between images and the speckle model; (2) the transformation model, i.e. the type of motion considered between images; (3) the optimization strategies, such as the use of different optimization techniques in the transformation step or the inclusion of structural information. While many contributions have shown their good performance independently, it is not always clear how they perform when integrated in a whole pipeline. Every step will have a degree of influence over the following and hence over the final result. Thus, a Speckle Tracking pipeline must be analyzed as a whole when developing novel methods, since improvements in a particular step might be undermined by the choices taken in further steps. This work presents two main contributions: (1) We provide a complete analysis of the influence of the different steps in a Speckle Tracking pipeline over the motion and strain estimation accuracy. (2) The study proposes a methodology for the analysis of Speckle Tracking systems specifically designed to provide an easy and systematic way to include other strategies. We close the analysis with some conclusions and recommendations that can be used as an orientation of the degree of influence of the models for speckle, the transformation models, interpolation schemes and optimization strategies over the estimation of motion features. They can be further use to evaluate and design new strategy into a Speckle Tracking system.

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