Blind source separation (BSS) for adaptive filtering is presented in application to imaging both physiological and acoustic radiation force impulse (ARFI)-induced tissue and blood motion in the common carotid artery. The collected raw radiofrequency (RF) data includes vessel wall motion, blood flow and ARFI-induced motion. In the context of these complex motion patterns, the same BSS adaptive filtering method was employed for three diverse applications: 1. clutter filtering ensembles prior to blood velocity estimation, 2. extracting small axial velocity components from noisy velocity measurements given large flow angles and 3. reducing noise in measured ARFI-induced tissue displacement profiles to enhance differentiation of local tissue structures. The filter separated physiological vessel wall motion from axial blood flow and ARFI-induced motion; successful filter performance is demonstrated in velocity estimates, color flow images and ARFI displacement profiles. The results demonstrate the breadth of applications for BSS adaptive filtering in the clinical imaging environment.