A Bayesian Approach to Eigenspectra Optoacoustic Tomography
Olefir I, Tzoumas S, Yang H, Ntziachristos V (2018)
Publication Type: Journal article
Publication year: 2018
Journal
Book Volume: 37
Pages Range: 2070-2079
Article Number: 8315499
Journal Issue: 9
DOI: 10.1109/TMI.2018.2815760
Abstract
The quantification of hemoglobin oxygen saturation (sO 2 ) with multispectral optoacoustic (OA) (photoacoustic) tomography (MSOT) is a complex spectral unmixing problem, since the OA spectra of hemoglobin are modified with tissue depth due to depth (location) and wavelength dependencies of optical fluence in tissue. In a recent work, a method termed eigenspectra MSOT (eMSOT) was proposed for addressing the dependence of spectra on fluence and quantifying blood sO 2 in deep tissue. While eMSOT offers enhanced sO 2 quantification accuracy over conventional unmixing methods, its performance may be compromised by noise and image reconstruction artifacts. In this paper, we propose a novel Bayesian method to improve eMSOT performance in noisy environments. We introduce a spectral reliability map, i.e., a method that can estimate the level of noise superimposed onto the recorded OA spectra. Using this noise estimate, we formulate eMSOT as a Bayesian inverse problem where the inversion constraints are based on probabilistic graphical models. Results: based on numerical simulations indicate that the proposed method offers improved accuracy and robustness under high noise levels due the adaptive nature of the Bayesian method.
Involved external institutions
How to cite
APA:
Olefir, I., Tzoumas, S., Yang, H., & Ntziachristos, V. (2018). A Bayesian Approach to Eigenspectra Optoacoustic Tomography. IEEE Transactions on Medical Imaging, 37(9), 2070-2079. https://doi.org/10.1109/TMI.2018.2815760
MLA:
Olefir, Ivan, et al. "A Bayesian Approach to Eigenspectra Optoacoustic Tomography." IEEE Transactions on Medical Imaging 37.9 (2018): 2070-2079.
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