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Olefir, I. ; Tzoumas, S.* ; Restivo, C. ; Mohajerani, P. ; Xing, L.* ; Ntziachristos, V.

Deep learning-based spectral unmixing for optoacoustic imaging of tissue oxygen saturation.

IEEE Trans. Med. Imaging 39, 3643-3654 (2020)
Postprint DOI
Open Access Green
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 (sO(2)) 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 sO(2) 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 sO(2) imaging is confirmed herein for the first time on ground truth sO(2) values experimentally obtained in vivo and ex vivo.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Optoacoustic/photoacoustic Imaging ; Multispectral Optoacoustic Tomography ; Photoacoustic Tomography ; Deep Learning ; Deep Neural Networks; Quantitative Photoacoustic Tomography; Blood Oxygenation; Reconstruction; Microscopy; Networks; Boundary
ISSN (print) / ISBN 0278-0062
e-ISSN 1558-254X
Quellenangaben Band: 39, Heft: 11, Seiten: 3643-3654 Artikelnummer: , Supplement: ,
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Verlagsort New York, NY [u.a.]
Begutachtungsstatus Peer reviewed
Förderungen National Institute of Health
European Research Council (ERC) through the European Union
Deutsche Forschungsgemeinschaft (DFG), Gottfried Wilhelm Leibniz Prize 2013
Deutsche Forschungsgemeinschaft (DFG), Sonderforschungsbereich