A robust method to measure viscosity of microquantities of biological samples, such as blood and mucus, could lead to a better understanding and diagnosis of diseases. Microsamples have presented persistent challenges to conventional rheology, which requires bulk quantities of a sample. Alternatively, fluid viscosity can be probed by monitoring microscale motion of particles. Here, we present a decorrelation-based method using M-mode phase-sensitive optical coherence tomography (OCT) to measure particle Brownian motion. This is similar to previous methods using laser speckle decorrelation but with sensitivity to nanometer-scale displacement. This allows for the measurement of decorrelation in less than 1 millisecond and significantly decreases sensitivity to bulk motion, thereby potentially enabling in vivo and in situ applications. From first principles, an analytical method is established using M-mode images obtained from a 47 kHz spectral-domain OCT system. A g(1) first-order autocorrelation is calculated from windows containing several pixels over a time frame of 200-1000 microseconds. Total imaging time is 500 milliseconds for averaging purposes. The autocorrelation coefficient over this short time frame decreases linearly and at a rate proportional to the diffusion constant of the particles, allowing viscosity to be calculated. In verification experiments using phantoms of microbeads in 200 µL glycerol-water mixtures, this method showed insensitivity to 2 mm/s lateral bulk motion and accurate viscosity measurements over a depth of 400 µm. In addition, the method measured a significant decrease of the apparent diffusion constant of soft tissue after formalin fixation, suggesting potential applications in mapping tissue stiffness.
Brecken J. Blackburn, Shi Gu, Michael W. Jenkins, and Andrew M. Rollins, "Decorrelation-based viscosity measurement using phase-sensitive optical coherence tomography (Conference Presentation)," Proc. SPIE 10067, Optical Elastography and Tissue Biomechanics IV, 1006711 (Presented at SPIE BiOS: January 30, 2017; Published: 24 April 2017); https://doi.org/10.1117/12.2252462.5380018845001.
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