This paper introduces strain-flow imaging as a potential new technique for investigating vascular dynamics and tumor biology. The deformation of tissues surrounding pulsatile vessels and the velocity of fluid in the vessel are estimated from the same data set. The success of the approach depends on the performance of a digital filter that must separate echo signal components caused by flow from tissue motion components that vary spatially and temporally. Eigenfilters, which are an important tool for naturally separating signal components adaptively throughout the image, perform very well for this task. The method is examined using two tissue-mimicking flow phantoms that provide stationary and moving clutter associated with pulsatile flow.

This paper introduces strain-flow imaging as a new technique for investigating vascular dynamics and tumor biology. The deformation of tissues surrounding pulsatile vessels and the velocity of fluid in the vessel are estimated from the same data set. The success of the approach depends on the performance of a digital filter that must separate echo signal components caused by flow from tissue motion components that vary spatially and temporally. Eigenfilters, which are an important tool for naturally separating signal components adaptively throughout the image, perform very well for this task. The method is examined using two tissue-mimicking flow phantoms that provide stationary and moving clutter associated with pulsatile flow.