  Locating vessels during surgery is critical for avoiding inadvertent damage,
yet vasculature can be difficult to identify. Video motion magnification can
potentially highlight vessels by exaggerating subtle motion embedded within the
video to become perceivable to the surgeon. In this paper, we explore a
physiological model of artery distension to extend motion magnification to
incorporate higher orders of motion, leveraging the difference in acceleration
over time (jerk) in pulsatile motion to highlight the vascular pulse wave. Our
method is compared to first and second order motion based Eulerian video
magnification algorithms. Using data from a surgical video retrieved during a
robotic prostatectomy, we show that our method can accentuate
cardio-physiological features and produce a more succinct and clearer video for
motion magnification, with more similarities in areas without motion to the
source video at large magnifications. We validate the approach with a Structure
Similarity (SSIM) and Peak Signal to Noise Ratio (PSNR) assessment of three
videos at an increasing working distance, using three different levels of
optical magnification. Spatio-temporal cross sections are presented to show the
effectiveness of our proposal and video samples are provided to demonstrates
qualitatively our results.
