This paper presents a novel model based segmentation technique for quantification of Left Ventricular (LV) function from sparse single-beat 3D echocardiographic data acquired with a Fast Rotating Ultrasound (FRU) transducer. This transducer captures cardiac anatomy in a sparse set of radially sampled, curved cross sections within a single cardiac cycle. The method employs a 3D Active Shape Model of the Left Ventricle (LV) in combination with local appearance models as prior knowledge to steer the segmentation. A set of local appearance patches generate the model update points for fitting the model to the LV in the curved FRU cross-sections. Updates are then propagated over the dense 3D model mesh to overcome correspondence problems due to the data sparsity, whereas the 3D Active Shape Model serves to retain the plausibility of the generated shape. Leave-one-out cross validation was carried out on single-beat FRU data from 28 patients suffering from various cardiac pathologies. Error measurements against expert-annotated contours yielded an average point-to-point distance of around 3.8 ± 2.4 mm and point-to-surface distance of 2.0 ± 1.8 mm and average volume estimation error of around 9 ± 7%. Robustness tests with respect to different model initializations showed acceptable performance for initial positions within a range of 22 mm for displacement and 12° for orientation. This demonstrates that the method combines robustness with respect to initialization with an acceptable accuracy, while using sparse single-beat FRU data.

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doi.org/10.1117/12.812490, hdl.handle.net/1765/57654
Department of Cardiology

Ma, M., van Stralen, M., Reiber, J., Bosch, H., & Lelieveldt, B. (2009). Model driven quantification of left ventricular function from sparse single-beat 3D echocardiography. doi:10.1117/12.812490