Quantitative photoacoustic oximetry using convolutional neural networks (Conference Presentation)

Kevin Hoffer-Hawlik; Austin Van Namen; Geoffrey P. Luke

doi:10.1117/12.2545197

6 March 2020 Quantitative photoacoustic oximetry using convolutional neural networks (Conference Presentation)

Kevin Hoffer-Hawlik, Austin Van Namen, Geoffrey P. Luke

Proceedings Volume 11240, Photons Plus Ultrasound: Imaging and Sensing 2020; 112402A (2020) https://doi.org/10.1117/12.2545197
Event: SPIE BiOS, 2020, San Francisco, California, United States

Abstract

Using spectroscopic photoacoustic imaging to quantitatively measure blood oxygenation saturation (sO2) is a difficult problem which requires prior tissue knowledge and costly computational methods. We have developed a convolutional neural network with a U-Net architecture to estimate the sO2 from spectroscopic photoacoustic data. The network was trained on Monte Carlo simulated spectroscopic PA data and predicted sO2 with only 4.49% error, an accuracy much higher than that of a linear spectral unmixing baseline. These results suggest that precise quantitative measurements of sO2 deep in tissue is attainable using machine learning approaches.

Conference Presentation

Citation Download Citation

Kevin Hoffer-Hawlik, Austin Van Namen, and Geoffrey P. Luke "Quantitative photoacoustic oximetry using convolutional neural networks (Conference Presentation)", Proc. SPIE 11240, Photons Plus Ultrasound: Imaging and Sensing 2020, 112402A (6 March 2020); https://doi.org/10.1117/12.2545197

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