Deep Learning-Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation

Olefir I, Tzoumas S, Restivo C, Mohajerani P, Xing L, Ntziachristos V (2020)

Publication Type: Journal article

Publication year: 2020

Journal

IEEE Transactions on Medical Imaging Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 39

Pages Range: 3643-3654

Journal Issue: 11

DOI: 10.1109/TMI.2020.3001750

Abstract

Label free imaging of oxygenation distribution in tissues is highly desired in numerous biomedical applications, but is still elusive, in particular in sub-epidermal measurements. Eigenspectra multispectral optoacoustic tomography (eMSOT) and its Bayesian-based implementation have been introduced to offer accurate label-free blood oxygen saturation (sO2) maps in tissues. The method uses the eigenspectra model of light fluence in tissue to account for the spectral changes due to the wavelength dependent attenuation of light with tissue depth. eMSOT relies on the solution of an inverse problem bounded by a number of ad hoc hand-engineered constraints. Despite the quantitative advantage offered by eMSOT, both the non-convex nature of the optimization problem and the possible sub-optimality of the constraints may lead to reduced accuracy. We present herein a neural network architecture that is able to learn how to solve the inverse problem of eMSOT by directly regressing from a set of input spectra to the desired fluence values. The architecture is composed of a combination of recurrent and convolutional layers and uses both spectral and spatial features for inference. We train an ensemble of such networks using solely simulated data and demonstrate how this approach can improve the accuracy of sO2 computation over the original eMSOT, not only in simulations but also in experimental datasets obtained from blood phantoms and small animals (mice) in vivo. The use of a deep-learning approach in optoacoustic sO2 imaging is confirmed herein for the first time on ground truth sO2 values experimentally obtained in vivo and ex vivo.

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich

Germany (DE) Stanford University

United States (USA) (US)

How to cite

APA:

Olefir, I., Tzoumas, S., Restivo, C., Mohajerani, P., Xing, L., & Ntziachristos, V. (2020). Deep Learning-Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation. IEEE Transactions on Medical Imaging, 39(11), 3643-3654. https://doi.org/10.1109/TMI.2020.3001750

MLA:

Olefir, Ivan, et al. "Deep Learning-Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation." IEEE Transactions on Medical Imaging 39.11 (2020): 3643-3654.

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