15 March 2016 Depth discrimination in acousto-optic cerebral blood flow measurement simulation
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Abstract
Monitoring cerebral blood flow (CBF) is crucial, as inadequate perfusion, even for relatively short periods of time, may lead to brain damage or even death. Thus, significant research efforts are directed at developing reliable monitoring tools that will enable continuous, bed side, simple and cost-effective monitoring of CBF. All existing non invasive bed side monitoring methods, which are mostly NIRS based, such as Laser Doppler or DCS, tend to underestimate CBF in adults, due to the indefinite effect of extra-cerebral tissues on the obtained signal. If those are to find place in day to day clinical practice, the contribution of extra-cerebral tissues must be eliminated and data from the depth (brain) should be extracted and discriminated. Recently, a novel technique, based on ultrasound modulation of light was developed for non-invasive, continuous CBF monitoring (termed ultrasound-tagged light (UTL or UT-NIRS)), and shown to correlate with readings of 133Xe SPECT and laser Doppler. We have assembled a comprehensive computerized simulation, modeling this acousto-optic technique in a highly scattering media. Using the combination of light and ultrasound, we show how depth information may be extracted, thus distinguishing between flow patterns taking place at different depths. Our algorithm, based on the analysis of light modulated by ultrasound, is presented and examined in a computerized simulation. Distinct depth discrimination ability is presented, suggesting that using such method one can effectively nullify the extra-cerebral tissues influence on the obtained signals, and specifically extract cerebral flow data.
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A. Tsalach, A. Tsalach, Z. Schiffer, Z. Schiffer, E. Ratner, E. Ratner, I. Breskin, I. Breskin, R. Zeitak, R. Zeitak, R. Shechter, R. Shechter, M. Balberg, M. Balberg, } "Depth discrimination in acousto-optic cerebral blood flow measurement simulation", Proc. SPIE 9708, Photons Plus Ultrasound: Imaging and Sensing 2016, 97083F (15 March 2016); doi: 10.1117/12.2211587; https://doi.org/10.1117/12.2211587
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