Model driven quantification of left ventricular function from sparse single-beat 3D echocardiography

Abstract

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. Detection succeeded in 24 cases, and failed in 4 cases due to large dropouts in echo signal. For the successful 24 cases, detection yielded Point to Point errors of 3.1+/-1.1mm, Point to Surface errors of 1.7+/-0.9mm and an EF error of 7.3+/-4.9%. Comparison of fitting on single-beat versus denser multi-beat data showed a similar performance for both types of data irrespective of frame angles of the intersections. Robustness tests with respect to different model initializations showed acceptable performance for initial positions within a range of 26mm for displacement and 12 degrees for orientation. Furthermore, a comparison study between the proposed method and global LV function measured from MR studies of the same patients showed an underestimation of volumes estimated from echocardiographic data compared to MR derived volumes, similar to other results reported in literature. All experiments demonstrate that the proposed method combines robustness with respect to initialization with an acceptable accuracy, while using sparse single-beat FRU data.