Auto svd clutter filtering for us doppler imaging using 3d clustering algorithm

Blood flow visualization is a challenging task in the presence of tissue motion. Conventional clutter filtering techniques perform poorly since blood and tissue clutter echoes share similar spectral characteristics. Thus, unsuppressed tissue clutter produces flashing artefacts in ultrasound color flow images. Eigen-based filtering was recently introduced and has shown good clutter rejection performance; however, there is yet no standard approach to robustly determine the eigen components corresponding to tissue clutter. To address this issue, we propose a novel 3D clustering based singular value decomposition (SVD) clutter filtering method. The proposed technique makes use of three key spatiotemporal statistics: singular value magnitude, spatial correlation and the mean Doppler frequency of singular vectors to adaptively determine the clutter and noise clusters and their corresponding eigen rank to achieve maximal clutter and noise suppression. To test the clutter rejection performance of the proposed filter, high frame rate plane wave data was acquired in-vivo from a subject’s common carotid artery and jugular vein region induced with extrinsic tissue motion (voluntary probe motion). The flow detection efficacy of the clustering based SVD filter was statistically evaluated and compared with current eigen rank estimation methods using the receiver operating characteristic (ROC) analysis. Results show that the clustering based SVD filter yielded the highest area under the ROC curve (0.9082) in comparison with other eigen rank estimation methods, signifying its improved flow detection capability.