Segmentation of Renal Perfusion Signals From Laser Speckle Imaging Into Clusters With Phase Synchronized Dynamics

Renal perfusion signals contain dynamics arising from the renal autoregulation feedback mechanisms as the contraction and dilation of vessels alter flow patterns. We can capture the time-varying dynamics at points across the renal surface using laser speckle imaging. We segment an imaged area of the renal cortex into clusters with phase synchronized dynamics. Our approach first uses phase coherence with a surrogate data derived threshold to identify synchronized pixel pairs. Non-negative matrix factorization is then applied to segment phase coherence estimates into phase synchronized regions. The method is applied to laser speckle imaging of the renal cortex of anaesthetized rats to identify regions on the renal surface with phase synchronized myogenic activity. In three out of six animals imaged after bolus infusion of N
$^{\omega}$
-nitro-l-arginine methyl ester (NAM), the renal surfaces are segmented into clusters with high phase coherence. No more than two clusters were identified during control period for any animal. In the remaining three animals, a strong myogenic signal could not be detected in surface perfusion during control or NAM. This method can be used to identify synchronization in renal autoregulation dynamics across the renal surface.