HighVis – A novel method for non-invasive optical imaging of blood viscosity in vivo

Blood viscosity is an independent risk factor for predicting cardiovascular diseases (CVD) including heart infarct and stroke. Routine assessment of blood viscosity can prevent progression to organ failure and subsequent premature mortality. Currently, fast, non-invasive techniques allowing repetitive measurement of viscosity in relevant patient populations are lacking, whereas existing ex-vivo techniques are cumbersome and not suitable for regular diagnostics.

In the HighVis project, we propose a novel approach to measure viscosity based on in vivo intravital microscopic imaging (MI) of dynamic blood flows in the microcirculation, and subsequent computational analysis of microcirculatory red blood cell behavior using a technique referred to as CFD-PIV. Time-dependent particle imaging velocimetry (PIV) data grids of microcirculation conditions in a set of tissues from several animal models are used as input for the computational modeling interface. The proposed algorithm efficiently solves an inverse problem based on non-Newtonian Navier-Stokes models and delivers blood dynamics characteristics including viscosity. In the clinical setting, in vivo non-invasive microcirculation blood flow characteristics can be captured in a variety of tissues including the buccal and sublingual microcirculation using hand-held vital microscopes. Applicability of our algorithm to clinically relevant settings requires integration with such currently available imaging modalities, and here we suggest how this could be assessed and achieved in follow-up studies. CVD disease progression is mirrored in the microcirculation, and analyzing the buccal and sublingual vasculature may thus provide a window for assessing overall patient health and therapeutic efficacy, which are essential steps toward personalized medicine.

The HighVis project is a collaborative initiative between LBRG and the le Noble Lab at the Zoological Institute (ZOO) at KIT.

This project is funded by a ZEISS Collaboration Catalyst grant initiated by ZEISS and KIT.