  Vascular networks can provide invaluable information about tumor
angiogenesis. Ultrafast Doppler imaging enables ultrasound to image
micro-vessels by applying tissue clutter filtering methods on the
Spatio-temporal data obtained from plane-wave imaging. However, motion is an
intrinsic part of microvasculature imaging due to various reasons e.g breathing
and vessel pulsation. Part of such a motion is taken care of by using
Spatio-temporal cluttering filtering. Nonetheless, the remaining part of the
motion who manifests itself as blurring or generating ghost vessels should be
corrected using another level of motion compensation. We proposed a robust and
computationally efficient motion compensation algorithm for Ultrasound
micro-vessel imaging. We successfully evaluated the performance of the
algorithm by a simulation study. Finally, we tested the proposed motion
compensation method on the in vivo data of microvasculature in different organs
including breast and thyroid. Results show blurring and ghost vessel problems
are significantly reduced using the proposed algorithm. Moreover, our
quantitative assessment demonstrated that image correlation among different
frames in Ultrafast Doppler imaging is significantly improved utilizing the
proposed motion correction method.
