Power-Doppler ultrasound (PD-US) imaging without contrast enhancement is being developed to routinely monitor for changes in blood perfusion. Although PD-US methods do not measure perfusion quantitatively, they can reliably indicate spatiotemporal variations in muscle perfusion once the clutter and noise power are sufficiently minimized. This paper explores a spatial registration method that is applied to echo signals prior to principal components analysis (PCA)-based clutter and noise filtering. The goal is to achieve PD-US images that predictably map relative perfusion. We use primarily echo-signal simulations to demonstrate sub-sample spatial registration of echo frames prior to clutter filtering over a range of tissue motion seen clinically. Registration narrows the eigen-spectrum of the tissue clutter component to a point where PCA filters are highly efficient at eliminating clutter power. However, the ability of the clutter filter to pass blood-signal power depends on the spatial patterns of blood cell movement in tissues. Prior in vivo studies have shown that symmetric Doppler spectra are most commonly observed for peripheral perfusion data. Symmetric spectra indicate nondirectional or diffuse perfusion patterns for which PD-US methods predictably pass 30-50% of the true blood-signal power. Given the unique features of peripheral perfusion imaging, spatial registration methods can significantly improve the reliability of PD-US imaging to represent tissue perfusion.
In this report, we explore the strengths and limitations of principal component analysis (PCA) and independent component analysis (ICA) for clutter and noise filtering in ultrasonic peripheral perfusion imaging. The advantages of pre-filtering spatial registration to reduce the bandwidth of coherent clutter motion is also considered. PCA methods excel when the echo covariance exhibits a significant blood-scattering component orthogonal to the tissue clutter component. This situation exists in peripheral perfusion imaging when the echo signals are temporally stationary and normally distributed. ICA methods separate non-orthogonal blood-clutter echo components often found in moving clutter, but only for echo signals with either non-normal-amplitude distributions or nonstationary normal distributions. When clutter movement is large and spatially coherent, echo registration followed by PCA filtering can be ideal. Effective filtering is essential for contrast-free ultrasonic perfusion imaging of muscle tissues in the extremities of patients at risk for developing peripheral artery diseases. Statistical filter performance is examined using simulation and echo data from an in vivo ischemic hindlimb mouse model.
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