Why the study?
Analyzing common carotid artery wall motion provides effective indicators for atherosclerosis, but optical flow-based methods tend to be impaired by feature drift.
A novel unscented Kalman filter-based data fusion approach improves the tracking of carotid artery wall motion in ultrasound sequences compared to standard optical flow methods.
Provides drift-resistant CCA wall tracking in ultrasound; leaves open clinical validation for atherosclerosis monitoring.
Analyzing the motion of the common carotid artery (CCA) wall yields effective indicators for atherosclerosis. In this work, we propose a state-space model and a tracking method for estimating the time-varying CCA wall radius from a B-mode ultrasound sequence of arbitrary length. We employ an unscented Kalman filter that fuses two sets of measurements produced by an optical flow algorithm and a CCA wall localization algorithm. This fusion-and-tracking approach ensures that feature drift, which tends to impair optical flow based methods, is compensated in a temporally consistent manner. Simulation results show that the proposed method outperforms a recently proposed optical flow based method.
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Dorazil et al. (2020) studied this question.
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