Abstract
Some optical properties of a highly scattering medium, such as tissue, can be reconstructed non-invasively by diffuse optical tomography (DOT). Since the inverse problem of DOT is severely ill-posed and nonlinear, iterative methods that update Green's function have been widely used to recover accurate optical parameters. However, recent research has shown that the joint sparse recovery principle can provide an important clue in achieving reconstructions without an iterative update of Green's function. One of the main limitations of the previous work is that it can only be applied to absorption parameter reconstruction. In this paper, we extended this theory to estimate the absorption and scattering parameters simultaneously when the background optical properties are known. The main idea for such an extension is that a joint sparse recovery step gives us unknown fluence on the estimated support set, which eliminates the nonlinearity in an integral equation for the simultaneous estimation of the optical parameters. Our numerical results show that the proposed algorithm reduces the cross-talk artifacts between the parameters and provides improved reconstruction results compared to existing methods.
Highlights
The near-infrared (NIR) optical wavelength range (700 ∼ 1000nm) provides an important opportunity for biological imaging, as tissues are relatively transparent in this regime due to the low absorption rate of the primary absorbers [1, 2]
We extended the results in our earlier work [13, 14] and propose a non-iterative method for the simultaneous reconstruction of both absorption and scattering parameter perturbation when the background optical properties are known
Based on the new formulation, we demonstrate that the reconstruction quality is improved and, that the cross-talk between two optical parameters can be reduced
Summary
The near-infrared (NIR) optical wavelength range (700 ∼ 1000nm) provides an important opportunity for biological imaging, as tissues are relatively transparent in this regime due to the low absorption rate of the primary absorbers [1, 2]. It is well-known that NIR light can penetrate tissue up to a depth of several centimeters. The computational overhead of iterative methods is often prohibitive, especially for 3-D imaging because updating the Green’s function requires multiple applications of 3-D partial differential equation (PDE) solvers or Monte Carlo simulation
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