Myocardial strain analysis of echocardiographic sequences using non-rigid registration

Ledesma-Carbayo, María J.; Santos, A.; Kybic, Jan; Mahia, Patricia; Garcı́a-Fernández, Miguel Ángel; Malpica, Norberto; Pérez-David, Esther; Desco, Manuel

doi:10.1109/cic.2004.1442935

Cited by 9 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We believe that the approaches presented by Tabassian et al [7,8] and Clarysse et al [9] are promising, as they provide more reliable TSC analysis and clinical diagnosis than the traditional approaches, which are extensively used by most technical papers analyzing performance comparison studies [2,[40][41][42][43] as well as by many clinical studies. [44][45][46] Yet, we also believe that a reliability determination of these results prior to any clinical diagnosis is essential.…”

Section: Discussionmentioning

confidence: 99%

“…Performance evaluation of these software packages is based mainly on the "one time point" global and segmental strain values, e.g. [40,42,43] Similarly, multicenter studies, such as Marwick et al, [2] and meta-analysis reports, such as, [41,46] which usually influence the clinical guidelines and define the normal and abnormal ranges of parameters, as well as clinical decisions, are also based on these "one time point" landmarks. In addition, many other clinical publications, [44,45] have investigated the potential of using such clinical strain landmarks, in addition to or instead of the conventional clinical parameters (such as ejection fraction, end systolic and end diastolic left ventricle areas/volumes, etc.).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Supervised learning approach for tracking quality determination of transmural and segmental time strain curves: A feasibility study

Khamis¹,

Yahav²,

Friedman³

et al. 2017

JBEI

View full text Add to dashboard Cite

Quantitative assessment of global and regional left ventricle function by means of myocardial strain estimation has been widely discussed as promising clinical diagnostic markers of left ventricular malfunction. These markers are provided to the clinicians without much feedback regarding their reliability, which may lead to erroneous diagnosis. Therefore, this study aims to classify the calculated strain curves into reliable or artefactual ones, before their clinical adaptation.A supervised machine learning approach is utilized for the classification process. A total of 6,552 strain curves were used, for which a visual labeling protocol was defined and utilized by two experts.An inter-observer labeling concordance of 93% was obtained, and a classification accuracy of 90% was achieved with a specificity of 92% and sensitivity of 78%.This classification tool may enhance the reliability of the estimations of global, transmural and regional strain curves, by automatically classifying them into physiological or artefactual curves.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Supervised learning approach for tracking quality determination of transmural and segmental time strain curves: A feasibility study

Khamis¹,

Yahav²,

Friedman³

et al. 2017

JBEI

View full text Add to dashboard Cite

show abstract

“…Basal septal seg (BS) 16 Apical lateral seg (AL) 11,12 Midlateral seg (ML) 5,6 Basal lateral seg (BL) …”

Section: Standard Number Area 17unclassified

“…Using magnetic resonance imaging (MRI), all its components (radial, longitudinal, and peripheral) are created simultaneously for all parts of the heart. However, this method is expensive and it takes a long time to collect the data [6,7].…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of coronary artery disease using registration of ultrasound images of the heart (echocardiography)

Masaeli¹,

Alavi²

2016

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Coronary artery diseases cause more than half of all deaths in the world. Obviously, early identification is an important way to control coronary artery disease that is diagnosed by measurement and scoring the general and regional movement of the left ventricle of heart (normal, hypokinetic, and akinetic). The most common method of imaging the heart using ultrasound is called echocardiography. Using this method accurate views of the heart walls, valves, and beginning of main arteries can be obtained. Due to the difficulty of the interpretation of these images, the length of time required, and errors in manual methods, an automated analysis method is required. In line with this goal, in this paper we have calculated the displacement field in a cycle of heart motion from two-dimensional echocardiography images. To do this, a frame is usually chosen as the reference frame and then all images in a cycle are mapped to it with a mathematical equation. The main idea is to find a semilocal spatiotemporal parametric model for deformation created in a cardiac cycle with nonrigid registration using B-spline functions as an optimization problem that effectively corrects differences due to movements by minimizing the difference between current frame and a reference frame. Motion estimation accuracy is measured using sum of squares differences. We use a gradient-descent algorithm and multiresolution method to acquire the coefficients in the motion model. The accuracy of the proposed method is assessed using a synthesis sequence of cardiac cycles produced with the simulation software Field II. This algorithm can be applied for the clinical analysis of the regional left ventricle and then movement parameters and threshold values for the scoring of each section can be extracted. The algorithm represents the significant difference between a part of a normal heart and an unhealthy heart and shows the potential of the clinical applications of the proposed method.

show abstract

“…Registration of ultrasound images can be used in estimating cardiac motion and/or velocities to find abnormalities in segmental wall motion and analyzing tissue mechanic properties such as quantification of elasticity and contractility of the myocardium [5]. It could be also used for spatial compounding of images to improve image quality and contrast [6].…”

Section: Introductionmentioning

confidence: 99%

Non-Rigid Ultrasound Image Registration Based on Intensity and Local Phase Information

Woo

Hong

et al. 2008

J Sign Process Syst Sign Image Video Technol

View full text Add to dashboard Cite

A non-rigid ultrasound image registration method is proposed in this work using the intensity as well as the local phase information under a variational framework. One application of this technique is to register two consecutive images in an ultrasound image sequence. Although intensity is the most widely used feature in traditional ultrasound image registration algorithms, speckle noise and lower image resolution make the registration process difficult. By integrating the intensity and the local phase information, we can find and track the non-rigid transformation of each pixel under diffeomorphism between the source and target images. Experiments using synthetic and cardiac images of in vivo mice and human subjects are conducted to demonstrate the advantages of the proposed method.

show abstract

Myocardial strain analysis of echocardiographic sequences using non-rigid registration

Cited by 9 publications

References 16 publications

Supervised learning approach for tracking quality determination of transmural and segmental time strain curves: A feasibility study

Supervised learning approach for tracking quality determination of transmural and segmental time strain curves: A feasibility study

Automatic detection of coronary artery disease using registration of ultrasound images of the heart (echocardiography)

Non-Rigid Ultrasound Image Registration Based on Intensity and Local Phase Information

Contact Info

Product

Resources

About