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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

Endocardial border detection in a 3D image. The borders are described as spatial coordinates (blue dots) in 3D. The initial position of the borders and the results after 10, 20, and 39 (final) results are shown
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Fig2: Endocardial border detection in a 3D image. The borders are described as spatial coordinates (blue dots) in 3D. The initial position of the borders and the results after 10, 20, and 39 (final) results are shown

Mentions: Detection of endocardial borders provides important clinical parameters such as volume, ejection fraction, and wall motion. Here, the active appearance model technique [8, 12] is adapted to detect borders in an end-diastolic 3D echocardiogram. The endocardial borders are modelled via spatial coordinates, distributed in a cylindrical and spherical representation which is suited to the shape of the left ventricle (Fig. 1). Image intensity values are mapped to a normal (Gaussian) distribution for appropriate statistical analysis [12]. The model represents variations of ventricular shapes and the typical appearance of the ventricle and myocardium in echocardiograms, including the typical artifacts. The model is trained for image analysis by estimating the relation between model parameters and image change via regression analysis: in the actual matching, the intensity difference between model and image is used to update the model’s parameters and drive the model closer to the image (Fig. 2). Evaluation on 99 patient images shows a successful matching in 91% of cases, with a median surface error of 2.65 mm (average 2.91 mm, standard deviation 1.03 mm)[13].Fig. 1


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)

Endocardial border detection in a 3D image. The borders are described as spatial coordinates (blue dots) in 3D. The initial position of the borders and the results after 10, 20, and 39 (final) results are shown
© Copyright Policy
Related In: Results  -  Collection

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

Fig2: Endocardial border detection in a 3D image. The borders are described as spatial coordinates (blue dots) in 3D. The initial position of the borders and the results after 10, 20, and 39 (final) results are shown
Mentions: Detection of endocardial borders provides important clinical parameters such as volume, ejection fraction, and wall motion. Here, the active appearance model technique [8, 12] is adapted to detect borders in an end-diastolic 3D echocardiogram. The endocardial borders are modelled via spatial coordinates, distributed in a cylindrical and spherical representation which is suited to the shape of the left ventricle (Fig. 1). Image intensity values are mapped to a normal (Gaussian) distribution for appropriate statistical analysis [12]. The model represents variations of ventricular shapes and the typical appearance of the ventricle and myocardium in echocardiograms, including the typical artifacts. The model is trained for image analysis by estimating the relation between model parameters and image change via regression analysis: in the actual matching, the intensity difference between model and image is used to update the model’s parameters and drive the model closer to the image (Fig. 2). Evaluation on 99 patient images shows a successful matching in 91% of cases, with a median surface error of 2.65 mm (average 2.91 mm, standard deviation 1.03 mm)[13].Fig. 1

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