Optical tomography using the time-independent equation of radiative transfer—Part 2: inverse model |
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Authors: | Alexander D. Klose Andreas H. Hielscher |
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Affiliation: | a Department of Pathology, State University of New York Downstate Medical Center, 450 Clarkson Avenue, Brooklyn, NY 11203, USA b Institut für Medizinische Physik, Klinikum Benjamin Franklin Freie, Universität Berlin, Fabeckstr. 60-62, 14195 Berlin, Germany c Department of Electrical and Computer Engineering, Polytechnic University, 5 Metrotech Center, Brooklyn, NY 11201, USA |
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Abstract: | Optical tomography is a novel imaging modality that is employed to reconstruct cross-sectional images of the optical properties of highly scattering media given measurements performed on the surface of the medium. Recent advances in this field have mainly been driven by biomedical applications in which near-infrared light is used for transillumination and reflectance measurements of highly scattering biological tissues. Many of the reconstruction algorithms currently utilized for optical tomography make use of model-based iterative image reconstruction (MOBIIR) schemes. The imaging problem is formulated as an optimization problem, in which an objective function is minimized. In the simplest case the objective function is a normalized-squared error between measured and predicted data. The predicted data are obtained by using a forward model that describes light propagation in the scattering medium given a certain distribution of optical properties.In part I of this two-part study, we presented a forward model that is based on the time-independent equation of radiative transfer. Using experimental data we showed that this transport-theory-based forward model can accurately predict light propagation in highly scattering media that contain void-like inclusions. In part II we focus on the details of our image reconstruction scheme (inverse model). A crucial component of this scheme involves the efficient and accurate determination of the gradient of the objective function with respect to all optical properties. This calculation is performed using an adjoint differentiation algorithm that allows for fast calculation of this gradient. Having calculated this gradient, we minimize the objective function with a gradient-based optimization method, which results in the reconstruction of the spatial distribution of scattering and absorption coefficients inside the medium. In addition to presenting the mathematical and numerical background of our code, we present reconstruction results based on experimentally obtained data from highly scattering media that contain void-like regions. These types of media play an important role in optical tomographic imaging of the human brain and joints. |
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Keywords: | Equation of radiative transfer Transport theory Inverse problems Adjoint model Adjoint method Adjoint differentiation Reverse differentiation Optical tomography Scattering media |
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