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Automated analysis of three-dimensional stress echocardiography.

Leung KY, van Stralen M, Danilouchkine MG, van Burken G, Geleijnse ML, Reiber JH, de Jong N, van der Steen AF, Bosch JG - Neth Heart J (2011)

Bottom Line: Methods for identifying anatomical views, detecting endocardial borders, and classification of wall motion are described and shown to provide favourable results.Interobserver agreement in wall motion scoring is better using the dedicated software (96%) than commercially available software not dedicated for this purpose (79%).The developed tools may provide useful quantitative and objective parameters to assist the clinical expert in the diagnosis of left ventricular function.

View Article: PubMed Central - PubMed

Affiliation: Erasmus MC, Thoraxcenter - Biomedical Engineering, Office Ee2302, P.O. Box 2040, 3000, CA, Rotterdam, the Netherlands.

ABSTRACT
Real-time three-dimensional (3D) ultrasound imaging has been proposed as an alternative for two-dimensional stress echocardiography for assessing myocardial dysfunction and underlying coronary artery disease. Analysis of 3D stress echocardiography is no simple task and requires considerable expertise. In this paper, we propose methods for automated analysis, which may provide a more objective and accurate diagnosis. Expert knowledge is incorporated via statistical modelling of patient data. Methods for identifying anatomical views, detecting endocardial borders, and classification of wall motion are described and shown to provide favourable results. We also present software developed especially for analysis of 3D stress echocardiography in clinical practice. Interobserver agreement in wall motion scoring is better using the dedicated software (96%) than commercially available software not dedicated for this purpose (79%). The developed tools may provide useful quantitative and objective parameters to assist the clinical expert in the diagnosis of left ventricular function.

No MeSH data available.


Related in: MedlinePlus

Modes of variation as calculated by Principal Component Analysis (PCA), which depict global variations in endocardial border motion, versus the local modes of variation after Orthomax rotation. The amount of the variation is colour-coded. The local modes are more concise and suitable for automatic classification of wall motion abnormalities
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Fig3: Modes of variation as calculated by Principal Component Analysis (PCA), which depict global variations in endocardial border motion, versus the local modes of variation after Orthomax rotation. The amount of the variation is colour-coded. The local modes are more concise and suitable for automatic classification of wall motion abnormalities

Mentions: Statistical models of the endocardial border motion throughout the cardiac cycle can be used to discriminate between normal and pathological motion patterns. The statistical model is transformed using the Orthomax rotation criterion, so that the local wall motion can be represented using fewer descriptors (Fig. 3) [6]. An automated classification method is then applied to the descriptors. Wall motion classification is demonstrated in 129 two-dimensional echocardiographic sequences. Using these models, local motion abnormalities can be detected accurately in 77–91% of the cases. In principle, the method can be applied directly to 3D borders; the evaluation is a subject of further investigation.Fig. 3


Automated analysis of three-dimensional stress echocardiography.

Leung KY, van Stralen M, Danilouchkine MG, van Burken G, Geleijnse ML, Reiber JH, de Jong N, van der Steen AF, Bosch JG - Neth Heart J (2011)

Modes of variation as calculated by Principal Component Analysis (PCA), which depict global variations in endocardial border motion, versus the local modes of variation after Orthomax rotation. The amount of the variation is colour-coded. The local modes are more concise and suitable for automatic classification of wall motion abnormalities
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3111551&req=5

Fig3: Modes of variation as calculated by Principal Component Analysis (PCA), which depict global variations in endocardial border motion, versus the local modes of variation after Orthomax rotation. The amount of the variation is colour-coded. The local modes are more concise and suitable for automatic classification of wall motion abnormalities
Mentions: Statistical models of the endocardial border motion throughout the cardiac cycle can be used to discriminate between normal and pathological motion patterns. The statistical model is transformed using the Orthomax rotation criterion, so that the local wall motion can be represented using fewer descriptors (Fig. 3) [6]. An automated classification method is then applied to the descriptors. Wall motion classification is demonstrated in 129 two-dimensional echocardiographic sequences. Using these models, local motion abnormalities can be detected accurately in 77–91% of the cases. In principle, the method can be applied directly to 3D borders; the evaluation is a subject of further investigation.Fig. 3

Bottom Line: Methods for identifying anatomical views, detecting endocardial borders, and classification of wall motion are described and shown to provide favourable results.Interobserver agreement in wall motion scoring is better using the dedicated software (96%) than commercially available software not dedicated for this purpose (79%).The developed tools may provide useful quantitative and objective parameters to assist the clinical expert in the diagnosis of left ventricular function.

View Article: PubMed Central - PubMed

Affiliation: Erasmus MC, Thoraxcenter - Biomedical Engineering, Office Ee2302, P.O. Box 2040, 3000, CA, Rotterdam, the Netherlands.

ABSTRACT
Real-time three-dimensional (3D) ultrasound imaging has been proposed as an alternative for two-dimensional stress echocardiography for assessing myocardial dysfunction and underlying coronary artery disease. Analysis of 3D stress echocardiography is no simple task and requires considerable expertise. In this paper, we propose methods for automated analysis, which may provide a more objective and accurate diagnosis. Expert knowledge is incorporated via statistical modelling of patient data. Methods for identifying anatomical views, detecting endocardial borders, and classification of wall motion are described and shown to provide favourable results. We also present software developed especially for analysis of 3D stress echocardiography in clinical practice. Interobserver agreement in wall motion scoring is better using the dedicated software (96%) than commercially available software not dedicated for this purpose (79%). The developed tools may provide useful quantitative and objective parameters to assist the clinical expert in the diagnosis of left ventricular function.

No MeSH data available.


Related in: MedlinePlus