Abstract
In this paper, we present a novel reconstruction method for diffuse optical spectroscopic imaging with a commonly used tissue model of optical absorption and scattering. It is based on linearization and group sparsity, which allows the diffusion coefficient and absorption coefficient to be recovered simultaneously, provided that their spectral profiles are incoherent and a sufficient number of wavelengths are judiciously taken for the measurements. We also discuss the reconstruction for an imperfectly known boundary and show that, with multi-wavelength data, the method can reduce the influence of modelling errors and still recover the absorption coefficient. Extensive numerical experiments are presented to support our analysis.
Highlights
Diffuse optical spectroscopy (DOS) is a non-invasive and quantitative medical imaging modality used for reconstructing absorption and scattering properties from optical measurements at multiple wavelengths excited by near-infrared (NIR) light
It is based on linearization and group sparsity, which allows the diffusion coefficient and absorption coefficient to be recovered simultaneously, provided that their spectral profiles are incoherent and a sufficient number of wavelengths are judiciously taken for the measurements
We show that the chromophore concentrations can still be reasonably recovered from multi-wavelength data, but the diffusion coefficient is lost owing to the corruption of the domain deformation
Summary
Diffuse optical spectroscopy (DOS) is a non-invasive and quantitative medical imaging modality used for reconstructing absorption and scattering properties from optical measurements at multiple wavelengths excited by near-infrared (NIR) light. There have been many important efforts to develop effective reconstruction algorithms using multi-wavelength data to obtain images and estimates of spatially varying concentrations of chromophores inside an optically scattering medium such as biological tissues. These include straightforward leastsquares minimization [4], models-based minimization [17] and the Bayesian approach [18]. We show that, within the linearized regime, incoherent spectral dependence allows the concentrations and diffusion coefficient to be recovered simultaneously, provided that a sufficient number of measurements are taken judiciously. Throughout, a vector always denotes a column vector, and the superscript t refers to the matrix/vector transpose
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