Model impact in resolving DCS CBF measurements from systemic variations in blood flow (Conference Presentation)

Melissa M. Wu; Davide Tamborini; Kimberly A. Stephens; Dibbyan Mazumder; Parya Farzam; Suk-Tak Chan; Jason Z. Qu; Maria A. Franceschini; Stefan A. Carp

doi:10.1117/12.2546699

9 March 2020 Model impact in resolving DCS CBF measurements from systemic variations in blood flow (Conference Presentation)

Melissa M. Wu, Davide Tamborini, Kimberly A. Stephens, Dibbyan Mazumder, Parya Farzam, Suk-Tak Chan, Jason Z. Qu, Maria A. Franceschini, Stefan A. Carp

Author Affiliations +

Proceedings Volume 11226, Neural Imaging and Sensing 2020; 112260X (2020) https://doi.org/10.1117/12.2546699
Event: SPIE BiOS, 2020, San Francisco, California, United States

Abstract

Diffuse correlation spectroscopy (DCS) is an increasingly widespread non-invasive technology to measure tissue perfusion. Extending this technique into adult brain monitoring to assess real-time cerebral blood flow (CBF) requires addressing the influence of extracerebral contributions on DCS measurements. We compare several Monte Carlo based forward simulation models on the efficacy of CBF isolation, including ones generated directly from individual subject MRI scans. We conclude that a multi-layer curved surface representation is beneficial, and that the traditional single-layer homogenous model is insufficient; however, detailed structural information such as cortical folding represented in an individualized tissue-specific model may not be needed.

Conference Presentation

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Melissa M. Wu, Davide Tamborini, Kimberly A. Stephens, Dibbyan Mazumder, Parya Farzam, Suk-Tak Chan, Jason Z. Qu, Maria A. Franceschini, and Stefan A. Carp "Model impact in resolving DCS CBF measurements from systemic variations in blood flow (Conference Presentation)", Proc. SPIE 11226, Neural Imaging and Sensing 2020, 112260X (9 March 2020); https://doi.org/10.1117/12.2546699

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KEYWORDS

Blood circulation

Natural surfaces

Magnetic resonance imaging

Tissue optics

Data modeling

Monte Carlo methods

Performance modeling

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