Automated analysis of three-dimensional stress echocardiography

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 [9].

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 [10], so that rest and stress images are aligned via minimisation of an objective criterion (in this case the correlation between image intensity values) [11]. 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 [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].

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 [14]. 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. 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.

To enable 3D stress echocardiography in routine clinical practice, we have also developed a software package dedicated to 3D stress echo [15]. The essential functionality for analysing 3D stress echo is investigated: side-by-side, synchronised display of anatomically aligned rest and stress images (Fig. 4). Two expert observers analysed 34 noncontrast clinically available rest and stress images visually. Interobserver agreement for segmental myocardial ischaemia is better using the dedicated software (96%) than commercially available software (79%) without these functionalities.