Adaptive Clutter Filtering for Speckle Decorrelation-Based Blood Flow Measurements

Abstract

Accurate measurement of blood flow velocity profiles is important for diagnosing diseases such as heart failure, carotid stenosis, and renal failure. Speckle decorrelation (SDC) was developed to measure 2D out-of-plane blood flow over the entire luminal area using a conventional 1D array transducer. One of the main challenges in SDC is the tissue clutter interference with blood. Adaptive clutter filters can suppress the clutter from backscattered signals in ultrasound blood-flow imaging. This study aims to evaluate the performance of adaptive clutter filters, including the discrete cosine transform (DCT), polynomial regression (PR), and singular value decomposition (SVD), for SDC-based blood flow velocity measurement via ultrasound simulations. Among the adaptive clutter filters, the SVD filter with Pareto scaling exhibits the best performance for clutter rejection and SDC-based flow velocity measurements. The benefit of this study is that it significantly improves the accuracy of SDC-based blood flow velocity measurements.