Experimental implementation of deep learning for blood oxygen saturation estimation

Sidhartha Jandhyala; Kevin Hoffer-Howlik; Ruibo Shang; Austin Van Namen; Geoffrey P. Luke

doi:10.1117/12.2583173

5 March 2021 Experimental implementation of deep learning for blood oxygen saturation estimation

Sidhartha Jandhyala, Kevin Hoffer-Howlik, Ruibo Shang, Austin Van Namen, Geoffrey P. Luke

Proceedings Volume 11642, Photons Plus Ultrasound: Imaging and Sensing 2021; 116421M (2021) https://doi.org/10.1117/12.2583173
Event: SPIE BiOS, 2021, Online Only

Abstract

Spectroscopic photoacoustic (sPA) imaging can be used to map blood oxygen saturation (sO2) within tissue. Its accuracy, however, is degraded deep in tissue by wavelength-dependent optical attenuation. We have developed a convolutional neural network to simultaneously estimate the sO₂ and segment blood vessels from sPA data. The network was trained on Monte Carlo simulated sPA data and predicted sO₂ with 9.31% median pixel error. The network was then retrained on experimental photoacoustic images of cow blood with median prediction error of 4.38%. These results suggest that precise quantitative measurements of sO₂ deep in tissue are attainable using machine learning approaches.

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Sidhartha Jandhyala, Kevin Hoffer-Howlik, Ruibo Shang, Austin Van Namen, and Geoffrey P. Luke "Experimental implementation of deep learning for blood oxygen saturation estimation", Proc. SPIE 11642, Photons Plus Ultrasound: Imaging and Sensing 2021, 116421M (5 March 2021); https://doi.org/10.1117/12.2583173

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KEYWORDS

Blood oxygen saturation

Blood

Tissue optics

Photoacoustic spectroscopy

Data modeling

Imaging systems

Monte Carlo methods

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