LV Motion Tracking from 3D Echocardiography Using Textural and Structural Information

Myronenko, Andriy; Song, Xubo; Sahn, David J.

doi:10.1007/978-3-540-75759-7_52

Cited by 32 publications

(27 citation statements)

References 14 publications

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“…In this paper, we describe a method to estimate the myocardial motion, from 3D ultrasound image sequence. The idea is to consecutively align 3D images one onto another and track the motion field using non-rigid image registration [2,3]. Spatially-dense displacement field can be derived, from which we compute quantitative measures such as strain and twist.…”

Section: Introductionmentioning

confidence: 99%

Maximum Likelihood Motion Estimation in 3D Echocardiography through Non-rigid Registration in Spherical Coordinates

Myronenko

Song

Sahn

2009

Functional Imaging and Modeling of the Heart

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Abstract. Automated motion tracking of the myocardium from 3D echocardiography provides insight into heart's architecture and function. We present a method for 3D cardiac motion tracking using nonrigid image registration. Our contribution is two-fold. We introduce a new similarity measure derived from a maximum likelihood perspective taking into account physical properties of ultrasound image acquisition and formation. Second, we use envelope-detected 3D echo images in the raw spherical coordinates format, which preserves speckle statistics and represents a compromise between signal detail and data complexity. We derive mechanical measures such as strain and twist, and validate using sonomicrometry in open-chest piglets. The results demonstrate the accuracy and feasibility of our method for studying cardiac motion.

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Section: Introductionmentioning

confidence: 99%

Maximum Likelihood Motion Estimation in 3D Echocardiography through Non-rigid Registration in Spherical Coordinates

Myronenko

Song

Sahn

2009

Functional Imaging and Modeling of the Heart

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“…It takes about 20 seconds for the whole motion sequence. PCT is at least two times faster than the slice-cut algorithm presented in [6] which is even not directly working on 3D volumetric data, three times faster than [31], and about hundreds of times faster than [32] which reported using a MATLAB implementation. …”

Section: A Endocardium Tracking Of the Left Ventriclementioning

confidence: 92%

Prediction Based Collaborative Trackers (PCT): A Robust and Accurate Approach Toward 3D Medical Object Tracking

Yang

Georgescu

Zheng

et al. 2011

IEEE Trans. Med. Imaging

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Abstract-Robust and fast 3D tracking of deformable objects, such as heart, is a challenging task because of the relatively low image contrast and speed requirement. Many existing 2D algorithms might not be directly applied on the 3D tracking problem. The 3D tracking performance is limited due to dramatically increased data size, landmarks ambiguity, signal drop-out or complex non-rigid deformation. In this paper we present a robust, fast and accurate 3D tracking algorithm: Prediction Based Collaborative Trackers (PCT). A novel onestep forward prediction is introduced to generate the motion prior using motion manifold learning. Collaborative trackers are introduced to achieve both temporal consistency and failure recovery. Compared with tracking by detection and 3D optical flow, PCT provides the best results. The new tracking algorithm is completely automatic and computationally efficient. It requires less than 1.5 seconds to process a 3D volume which contains millions of voxels. In order to demonstrate the generality of PCT, the tracker is fully tested on three large clinical datasets for three 3D heart tracking problems with two different imaging modalities: endocardium tracking of the left ventricle (67 sequences, 1134 3D volumetric echocardiography data), dense tracking in the myocardial regions between the epicardium and endocardium of the left ventricle (503 sequences, roughly 9000 3D volumetric echocardiography data), and whole heart four chambers tracking (20 sequences, 200 cardiac 3D volumetric CT data). Our datasets are much larger than most studies reported in the literature and we achieve very accurate tracking results compared with human experts' annotations and recent literature.

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“…Others hybrid similarity measures such as the sum of squared differences combined with structural information (Myronenko et al, 2007) or the sum of squared differences combined with Rayleigh model were proposed in order to jointly consider the statistical characterization of speckle and some local structural information assuming that "structural information …”

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%

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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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LV Motion Tracking from 3D Echocardiography Using Textural and Structural Information

Cited by 32 publications

References 14 publications

Maximum Likelihood Motion Estimation in 3D Echocardiography through Non-rigid Registration in Spherical Coordinates

Maximum Likelihood Motion Estimation in 3D Echocardiography through Non-rigid Registration in Spherical Coordinates

Prediction Based Collaborative Trackers (PCT): A Robust and Accurate Approach Toward 3D Medical Object Tracking

Influence of ultrasound speckle tracking strategies for motion and strain estimation

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