stress echocardiography ( stress echo ) is widely applied for evaluating cardiac function [ 1 , 2 ] .
motion of the left ventricle is assessed by comparing ultrasound images taken in rest and in stress stages . in the past decade , real - time three - dimensional
( 3d ) imaging has become available for quantitative and objective assessment of cardiac function [ 3 , 4 ] .
3d imaging offers the opportunity to overcome the limitations of traditional two - dimensional stress echo . with 3d imaging , non - foreshortened anatomical views
however , due to suboptimal image quality , lower spatial and temporal resolution , and the sheer amount of data , quantitative analysis by hand is tedious and subjective .
automated analysis methods may allow objective and faster assessment of clinical parameters such as left ventricular volume , ejection fraction and wall motion . in practice
, the clinician makes a diagnosis using expert knowledge , gathered by analysing many patient images [ 6 , 7 ] . to develop successful automated methods which can deal with different pathologies and varying image quality
, we present automated methods for 3d stress echo which make use of knowledge gained from databases of patient data .
the knowledge is encompassed in so - called statistical models . using principal component analysis ,
patient variability can be represented concisely as mathematical descriptors that form a vector of orthogonal parameters . the parameters to be modelled ( for example , spatial coordinates of the endocardial border )
essentially , the methods are geared towards fitting the image data at hand to the model . in other words ,
the variation is estimated according to a set of extra criteria ( such as image intensity values , 
our proposed analysis scheme consists of three steps : identification of correct anatomical views in 3d data , detection of endocardial borders from which motion can be derived , and automated classification of motion .
in addition , we present a software package which we have developed dedicated to analysis of 3d stress echo .
first we describe a method for detecting four - chamber , two - chamber , and short - axis views in a 3d image .
these views are modelled via appearance ( image intensity values ) and pose parameters ( translation , rotation , and scaling ) .
these parameters are identified by fitting the database model to the image at hand within an optimisation framework .
the views in a rest image can be used to extract the anatomically corresponding views in a stress image of the same patient .
this is achieved via image registration , so that rest and stress images are aligned via minimisation of an objective criterion ( in this case the correlation between image intensity values ) .
qualitative and quantitative evaluation in 20 end - diastolic and 20 end - systolic images shows that the views found are in many cases better ( 18% ) or similar ( 75% ) to the manual identification of views .
anatomically aligned views are thus obtained automatically , providing more possibilities for accurate visualisation and quantification .
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 .
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 ) . 

1modes of variations of an appearance model , created by varying the model descriptors one at a time .
the appearance model consists of a  shape ( spatial coordinates ) and a  texture ( image intensity values ) component .
the average and the three most principal modes ( 2 standard deviations ) are shownfig . 
the initial position of the borders and the results after 10 , 20 , and 39 ( final ) results are shown modes of variations of an appearance model , created by varying the model descriptors one at a time .
shape ( spatial coordinates ) and a  texture ( image intensity values ) component .
the average and the three most principal modes ( 2 standard deviations ) are shown endocardial border detection in a 3d image .
the initial position of the borders and the results after 10 , 20 , and 39 ( final ) results are shown the borders throughout the cardiac cycle are obtained by tracking the motion of the cardiac wall .
a 3d optical flow algorithm is applied , which uses spatial and temporal intensity gradients to track the endocardial border between two time frames . to ensure an overall temporal continuity ,
the tracking method takes into account physically plausible motion , learned statistically from a patient database .
typical image artifacts , such as echo drop - out and near - field artifacts , are detected and dealt with appropriately using an expectation maximisation based classification approach .
the method is capable of tracking the endocardial borders accurately ( in 35 patients : surface errors : 1.2    0.5  mm , volume errors : 1.4    6.7  ml , ejection fraction errors : 0.9    4.8% ) .
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 . 
wall motion classification is demonstrated in 129 two - dimensional echocardiographic sequences . using these models ,
, the method can be applied directly to 3d borders ; the evaluation is a subject of further investigation . 

3modes 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 local modes are more concise and suitable for automatic classification of wall motion abnormalities 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 .
to enable 3d stress echocardiography in routine clinical practice , we have also developed a software package dedicated to 3d stress echo .
the essential functionality for analysing 3d stress echo is investigated : side - by - side , synchronised display of anatomically aligned rest and stress images ( fig .  4 ) .
interobserver agreement for segmental myocardial ischaemia is better using the dedicated software ( 96% ) than commercially available software ( 79% ) without these functionalities . 

4software dedicated to 3d stress analysis , allowing side - by - side viewing of images acquired at rest and in varying stages of stress .
anatomical four - chamber , two - chamber , short - axis , and long - axis views are shown software dedicated to 3d stress analysis , allowing side - by - side viewing of images acquired at rest and in varying stages of stress .
anatomical four - chamber , two - chamber , short - axis , and long - axis views are shown
in this paper , we have presented automated methods for identification of anatomical views , detection of endocardial borders , and classification of wall motion abnormalities .
in particular , these methods are better capable of handling typical image artifacts and following expert tracing conventions .
we have also demonstrated the advantages of using dedicated software for the analysis of 3d stress echo in the clinical setting . based on this research ,
opportunities remain for further development , e.g. in the analysis of contrast echocardiograms , technical enhancement of image quality and spatiotemporal resolution , and improvement of clinical workflow .
the developed tools can provide useful quantitative and objective parameters to help the clinical expert in the diagnosis of left ventricular function .
 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 . 
Introduction
Identification of anatomical views
Quantification of volume and wall motion
Automated motion classification
Tools for clinical practice
Conclusion