Adaptive clutter filtering via blind source separation for two-dimensional ultrasonic blood velocity measurement.
A method for adaptive clutter rejection via blind source separation (BSS) using principal and independent component analyses is presented in application to blood velocity measurement in the carotid artery. In particular, the filtering method's efficacy for eliminating clutter and preserving lateral blood flow signal components is presented. The performance of IIR filters is compromised by shorth data ensembles (10 to 20 temporal samples) as implemented for color-flow and high frame-rate imaging due to initialization requirements. Further, the ultrasonic imaging system's transfer function maps axial wall and lateral blood motion to overlapping spectra. As such, frequency domain-based approaches to wall filtering are ineffective for distinguishing wall from blood motion signals. Rather than operating in the frequency domain. BSS performs clutter rejection by decomposing the input data ensemble into N constitutive source signals in time, where N is the ensemble length. Source signal energy coupled with respective signal depth and time course profiles reveal which source signals correspond to blood, noise and clutter components. Clutter components may then be removed without disruption of lateral blood flow information needed for two-dimensional blood velocity measurement. A simplistic data simulation is employed to offer an intuitive understanding of BSS methods for signal separation. The adaptive BSS filter is further demonstrated using a Field II simulation of blood flow through the carotid artery including tissue motion. BSS clutter filter performance is compared to the performance of FIR, IIR and polynomial regression clutter filters. Finally, the filter is employed for clinical application using a Siemens Elegra scanner, carotid artery data with lateral blood flow collected from healthy volunteers, and Speckle Tracking; velocity magnitude and angle profiles are shown. Once again, the BSS clutter filter is contrasted to FIR, IIR and polynomial regression clutter filters using clinical examples. Velocities computed with Speckle Tracking after BSS wall filtering are highest in the center of the artery and diminish to low velocities near the vessel walls, with velocity magnitudes consistent with physiological expectations. These results demonstrate that the BSS adaptive filter sufficiently suppresses wall motion signal for clinical lateral blood velocity measurement using data ensembles suitable for color-flow and high frame-rate imaging.
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