Diffuse optical tomography of the brain: effects of inaccurate baseline optical parameters and refinements using learned post-processing
Mozumder, Meghdoot; Hirvi, Pauliina; Nissilä, Ilkka; Hauptmann, Andreas; Ripoll, Jorge; Singh, David E. (2024-07-05)
Optica Publishing Group
05.07.2024
Meghdoot Mozumder, Pauliina Hirvi, Ilkka Nissilä, Andreas Hauptmann, Jorge Ripoll, and David E. Singh, "Diffuse optical tomography of the brain: effects of inaccurate baseline optical parameters and refinements using learned post-processing," Biomed. Opt. Express 15, 4470-4485 (2024)
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© 2024 Optica Publishing Group. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.
https://rightsstatements.org/vocab/InC/1.0/
© 2024 Optica Publishing Group. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202505263919
https://urn.fi/URN:NBN:fi:oulu-202505263919
Tiivistelmä
Abstract
Diffuse optical tomography (DOT) uses near-infrared light to image spatially varying optical parameters in biological tissues. In functional brain imaging, DOT uses a perturbation model to estimate the changes in optical parameters, corresponding to changes in measured data due to brain activity. The perturbation model typically uses approximate baseline optical parameters of the different brain compartments, since the actual baseline optical parameters are unknown. We simulated the effects of these approximate baseline optical parameters using parameter variations earlier reported in literature, and brain atlases from four adult subjects. We report the errors in estimated activation contrast, localization, and area when incorrect baseline values were used. Further, we developed a post-processing technique based on deep learning methods that can reduce the effects due to inaccurate baseline optical parameters. The method improved imaging of brain activation changes in the presence of such errors.
Diffuse optical tomography (DOT) uses near-infrared light to image spatially varying optical parameters in biological tissues. In functional brain imaging, DOT uses a perturbation model to estimate the changes in optical parameters, corresponding to changes in measured data due to brain activity. The perturbation model typically uses approximate baseline optical parameters of the different brain compartments, since the actual baseline optical parameters are unknown. We simulated the effects of these approximate baseline optical parameters using parameter variations earlier reported in literature, and brain atlases from four adult subjects. We report the errors in estimated activation contrast, localization, and area when incorrect baseline values were used. Further, we developed a post-processing technique based on deep learning methods that can reduce the effects due to inaccurate baseline optical parameters. The method improved imaging of brain activation changes in the presence of such errors.
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