Computer Analysis of Heart Motion from Two-Dimensional Echocardiograms

Mailloux, G.E.; Bleau, André; Bertrand, Michel; Petitclerc, Robert

doi:10.1109/tbme.1987.325967

Cited by 151 publications

(64 citation statements)

References 8 publications

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“…This texture information in cardiac images can be analyzed using the principles of optical flow (24) and originally were applied to 2D motion, as for cine ultrasound images of the heart (25,26). In this method, intensity features in an image plane are assumed to persist through multiple times and not change their intensity, called the brightness constraint.…”

Section: Methods Of Cardiac Motion Detectionmentioning

confidence: 99%

Quantitative Tagged Magnetic Resonance Imaging of the Normal Human Left Ventricle

Moore¹,

McVeigh²,

Zerhouni³

2000

Topics in Magnetic Resonance Imaging

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SummaryMagnetic resonance imaging with tissue tagging is a noninvasive technique for measuring threedimensional motion and deformation in the human heart. Tags are regions of tissue whose longitudinal magnetization has been altered before imaging so that they appear dark in subsequent magnetic resonance images. They then move with the underlying tissue and serve as easily identifiable landmarks within the heart for the detailed detection of motion. Many different motion and strain parameters can be determined from tagged magnetic resonance imaging. Strain components that are based on a high density of tag data, such as circumferential and longitudinal shortening, or parameters that are combinations of multiple strain components, have highest measurement precision and tightest normal ranges. The pattern of three-dimensional motion and strain in the heart is important clinically, because it reflects the basic mechanical function of the myocardium at both local and global levels. Localized abnormalities can be detected and quantified if the pattern of deformation in a given heart is compared to the normal range for that region, because normal motion and strain in the left ventricle is spatially heterogeneous. Contraction strains typically are greatest in the anterior and lateral walls and increase toward the apex. The direction of greatest contraction lies along a counter clockwise helix from base to apex (viewed from the base) and approximates the epicardial muscle fiber direction. This fiber geometry also results in long-axis torsion during systole. Ejection is accomplished primarily by radially inward motion of the endocardium and by descent of the base toward the apex during systole. KeywordsMagnetic resonance imaging; Human heart; Three-dimensional strain; Tissue tagging Accurate three-dimensional (3D) measurement of myocardial deformation throughout the heart during contraction is critical to a fundamental understanding of myocardial function (1). Such an analysis is valuable clinically because most ischemic heart disease, the leading cause of death in the United States, typically affects localized regions of the myocardium (2). The time-resolved local strain map of a heart, as compared to a normal database, permits quantification of both the degree and the physical extent of mechanical dysfunction. In addition, it can be used to improve cardiac analytic and finite element models (3-5), which may help characterize tissue properties and predict the effects on the heart of specific abnormalities and surgical treatments.There are two main problems associated with measuring cardiac deformation. First is the scarcity of readily identifiable landmarks on, and especially within, the heart wall. The second difficulty is that the complex 3D motions of the heart through tomographic images prevent Address correspondence and reprint requests to Dr. Christopher C. Moore, Department of Radiology, The Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Baltimore, MD 21287, U.S.A.. NIH Public Access Author Man...

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Section: Methods Of Cardiac Motion Detectionmentioning

confidence: 99%

Quantitative Tagged Magnetic Resonance Imaging of the Normal Human Left Ventricle

Moore¹,

McVeigh²,

Zerhouni³

2000

Topics in Magnetic Resonance Imaging

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“…Indeed, the computation of optical flow restricted on the points on the cavity walls, provides a good description of the dynamics of the heart and therefore of its functional state. In previous studies the computation of the optical flow on echocardiographic image sequences was impaired by several problems because of noise [12]. The computation of the flow over the entire image requires also a long computing time, which is not suitable for real-time clinical application.…”

Section: Discussionmentioning

confidence: 99%

“…However, some systems are able to produce image processing helpful to enhance the definition of the heart cavities and to provide some other useful parameters [4]. Computer Vision tools have been used in pilot studies to perform heart cavity detection: interpolations of calculated edges [5,9], snakes [7,9] and segmentation techniques based on Markov Random Fields [10] have already been exploited, while optical flow has been used to study heart motion [12,13]. In this paper we describe some preliminary results of a system for the automatic analysis of echocardiographic images, based on the use of snakes [6,7] and the computation of optical flow [11].…”

Section: Introductionmentioning

confidence: 99%

Computer assisted analysis of echocardiographic image sequences

Giachetti

Cappello

Gigli³

et al. 1995

Image Analysis and Processing

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Abstract. In this paper we present a semi-automatic system for the analysis of echocardiographic image sequences, able to provide useful information to cardiologists. The proposed approach combines well known techniques for the detection of left ventricular boundaries with the computation of optical flow. The initial detection of the cavity contour is based on an improved balloon model, the computation of optical flow is performed with a correlation technique and the contour tracking is obtained combining motion information provided by the optical flow with a snake-based regularization. The system is able to follow precisely the cavities motion, to provide several quantitative features of the heart beat and a dynamic representation of systolic and diastolic motion. Preliminary experimental results are presented and commented with particular attention to their clinical relevance.

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“…Following the optimization approach studied in [28], we use the implicit Euler time marching method [48] to solve (17). At each iteration, the displacement u is estimated as…”

Section: ) Motion Field Estimationmentioning

confidence: 99%

Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning

Ouzir

Basarab

Liebgott

et al. 2018

IEEE Trans. on Image Process.

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract-This paper introduces a new method for cardiac motion estimation in 2-D ultrasound images. The motion estimation problem is formulated as an energy minimization, whose data fidelity term is built using the assumption that the images are corrupted by multiplicative Rayleigh noise. In addition to a classical spatial smoothness constraint, the proposed method exploits the sparse properties of the cardiac motion to regularize the solution via an appropriate dictionary learning step. The proposed method is evaluated on one data set with available ground-truth, including four sequences of highly realistic simulations. The approach is also validated on both healthy and pathological sequences of in vivo data. We evaluate the method in terms of motion estimation accuracy and strain errors and compare the performance with state-of-the-art algorithms. The results show that the proposed method gives competitive results for the considered data. Furthermore, the in vivo strain analysis demonstrates that meaningful clinical interpretation can be obtained from the estimated motion vectors.

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Computer Analysis of Heart Motion from Two-Dimensional Echocardiograms

Cited by 151 publications

References 8 publications

Quantitative Tagged Magnetic Resonance Imaging of the Normal Human Left Ventricle

Quantitative Tagged Magnetic Resonance Imaging of the Normal Human Left Ventricle

Computer assisted analysis of echocardiographic image sequences

Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning

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