A Machine Learning Approach to Determining Sub-Diffuse Optical Properties

Andrew C. Stier; Will Goth; Yao Zhang; Matthew C. Fox; Jason S. Reichenberg; Fabiana C.P.S. Lopes; Katherine R. Sebastian; Mia K. Markey; Mia K. Markey; James W. Tunnell

doi:10.1364/OTS.2020.SM2D.6

Biophotonics Congress: Biomedical Optics 2020 (Translational, Microscopy, OCT, OTS, BRAIN)
OSA Technical Digest (Optica Publishing Group, 2020),
paper SM2D.6
•https://doi.org/10.1364/OTS.2020.SM2D.6

A Machine Learning Approach to Determining Sub-Diffuse Optical Properties

Andrew C. Stier, Will Goth, Yao Zhang, Matthew C. Fox, Jason S. Reichenberg, Fabiana C.P.S. Lopes, Katherine R. Sebastian, Mia K. Markey, and James W. Tunnell

Optical Tomography and Spectroscopy 2020

Washington, DC United States
20–23 April 2020
ISBN: 978-1-943580-74-3

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Cancer and Therapy Monitoring with Diffuse Optics and Beyond (SM2D)

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A. C. Stier, W. Goth, Y. Zhang, M. C. Fox, J. S. Reichenberg, F. C. P. S. Lopes, K. R. Sebastian, M. K. Markey, and J. W. Tunnell, "A Machine Learning Approach to Determining Sub-Diffuse Optical Properties," in Biophotonics Congress: Biomedical Optics 2020 (Translational, Microscopy, OCT, OTS, BRAIN), OSA Technical Digest (Optica Publishing Group, 2020), paper SM2D.6.

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Abstract

We demonstrate a machine learning approach that renders real-time subdiffuse optical property (γ) maps from spatial frequency domain images, laying the foundation for use in surgical guidance.

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