Monitoring of rhythms in laset speckle data

While the laser speckle imaging(LSI)is a powerful tool for multiple biomedical applications,suchas monitoring of the blood flow,in many cases it can provide additional information when combined with spatio-temporal rhythm analysis.We demonstrate the application of GraphicsProcessing Units(GPU)-based rhythm analysis for the post procesing of LSI data,discuss therelevant structure of GPU-based computations,test the proposed technique on surrogate 3D data,and apply this approach to kidney blood fiow autoregulation.Experiments with surrogate data demonstrate the ability of the method to extract information about oscillation patterns fromnoisy data,as well as to detect the moving source of the rhythm.The analysis of kidney dataallow us to detect and to localize the dymamics arising from autoregulation processes at the levelof individual nephrons(tubuloglomerular feedback(TGF)rhythm),as well as to distinguishbetween the TGF-active and the TGF-silent zones.