Development of an in silico NIRS model to inform the development of performance test methods

Brandon Gaitan; Alice Truong; Mousa Moradi; Yu Chen; Joshua Pfefer

doi:10.1117/12.2609971

7 March 2022 Development of an in silico NIRS model to inform the development of performance test methods

Brandon Gaitan, Alice Truong, Mousa Moradi, Yu Chen, Joshua Pfefer

Proceedings Volume PC11951, Design and Quality for Biomedical Technologies XV; PC1195107 (2022) https://doi.org/10.1117/12.2609971
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

Computational modeling provides a powerful tool for identifying optimal phantom-based test methods in NIRS oximetry. We implemented a Monte Carlo model to enable the simulation of NIRS devices with specific illumination-collection geometries and to identify appropriate performance test methods. Initially, we validated that our in silico approach provided adequate convergence and identified a phantom size that was optically semi-infinite. We then assessed the impact of NIRS sensor orientations and positions on a simulated channel-array phantom. Additional simulations are currently underway to extract oxygen saturation and determine the effect of phantom layer thicknesses, vessel spacing, and diameter.

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Brandon Gaitan, Alice Truong, Mousa Moradi, Yu Chen, and Joshua Pfefer "Development of an in silico NIRS model to inform the development of performance test methods", Proc. SPIE PC11951, Design and Quality for Biomedical Technologies XV, PC1195107 (7 March 2022); https://doi.org/10.1117/12.2609971

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KEYWORDS

Near infrared spectroscopy

Performance modeling

Monte Carlo methods

Signal detection

Oximeters

Reflectivity

Sensors

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