Distribution-informed and wavelength-flexible data-driven photoacoustic oximetry

Janek Gröhl; Kylie Yeung; Kevin Gu; Thomas R. Else; Monika Golinska; Ellie V. Bunce; Lina Hacker; Sarah E. Bohndiek

doi:10.1117/1.JBO.29.S3.S33303

5 June 2024 Distribution-informed and wavelength-flexible data-driven photoacoustic oximetry

Janek Gröhl, Kylie Yeung, Kevin Gu, Thomas R. Else, Monika Golinska, Ellie V. Bunce, Lina Hacker, Sarah E. Bohndiek

Author Affiliations +

Journal of Biomedical Optics, Vol. 29, Issue S3, S33303 (June 2024). https://doi.org/10.1117/1.JBO.29.S3.S33303

Abstract

Significance

Photoacoustic imaging (PAI) promises to measure spatially resolved blood oxygen saturation but suffers from a lack of accurate and robust spectral unmixing methods to deliver on this promise. Accurate blood oxygenation estimation could have important clinical applications from cancer detection to quantifying inflammation.

Aim

We address the inflexibility of existing data-driven methods for estimating blood oxygenation in PAI by introducing a recurrent neural network architecture.

Approach

We created 25 simulated training dataset variations to assess neural network performance. We used a long short-term memory network to implement a wavelength-flexible network architecture and proposed the Jensen–Shannon divergence to predict the most suitable training dataset.

Results

The network architecture can flexibly handle the input wavelengths and outperforms linear unmixing and the previously proposed learned spectral decoloring method. Small changes in the training data significantly affect the accuracy of our method, but we find that the Jensen–Shannon divergence correlates with the estimation error and is thus suitable for predicting the most appropriate training datasets for any given application.

Conclusions

A flexible data-driven network architecture combined with the Jensen–Shannon divergence to predict the best training data set provides a promising direction that might enable robust data-driven photoacoustic oximetry for clinical use cases.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Janek Gröhl, Kylie Yeung, Kevin Gu, Thomas R. Else, Monika Golinska, Ellie V. Bunce, Lina Hacker, and Sarah E. Bohndiek "Distribution-informed and wavelength-flexible data-driven photoacoustic oximetry," Journal of Biomedical Optics 29(S3), S33303 (5 June 2024). https://doi.org/10.1117/1.JBO.29.S3.S33303

Received: 19 March 2024; Accepted: 17 May 2024; Published: 5 June 2024

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KEYWORDS

Education and training

Computer simulations

Blood

In vivo imaging

Tissues

Error analysis

Oximetry

Significance

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