| dc.description.abstract | Diffuse optical tomography (DOT), in which laser light enters the tissue, and the light information is collected by detector. There are two parts in the image reconstruction: forward calculation and inverse reconstruction. The forward calculation uses the finite element method (FEM) to solve the diffusion equation to present the light transmission in the tissue and obtain the light information at different positions; The inverse calculation was performed by Newton′s method to minimize the difference between the measured data and the forward calculation, to reconstruct the tissue optical coefficient distribution, and to determine the size and location of the tumor. Since the inverse calculation is a non-linear and ill-conditioned problem, Tikhonov′s regularization is added to limit the range of solutions, and the regularization term is used to stabilize the reconstruction results.
In the reconstruction of DOI images, it is not easy to manually adjust the regularization parameters during the iterative band calculation, and the selection affects the image quality. In this paper, we conduct numerical simulations and experiments with different imitations of object size, centroid distance, and optical coefficient contrast by driving the light source with single frequency and multi-frequency power sources, and then evaluate and analyze the image reconstruction with the selected fixed regularization parameters and the regularization parameters obtained from the L-curve and U-curve method. The L-curve method has the minimum solution and residual norm as the optimal regular parameter, but the minimum value of both is not necessarily balanced, so that the L-curve shows an inverted L-shape in the next iteration, and the optimal regular parameter cannot be obtained, resulting in an overfitting state; The U-curve method is able to reconstruct the position of the object in both simulation and experimental results, and can effectively suppress noise. In the image reconstruction with fixed regular parameters, overfitting often occurs when the regular parameter is 0.02, underfitting occurs when the regular parameter is 50, and the position of the object can be reconstructed roughly when the regular parameter is 1. Using CSD to analyze the simulated and experimental data images, it was found that the U curve method obtained the best absorption coefficient resolution in 3 cases and the best scattering coefficient resolution in 7 cases in the simulated results, while the experimental results obtained the best absorption coefficient resolution in 1 case and the best scattering coefficient resolution in 2 cases, and the U curve method obtained better results from either absorption or scattering coefficient resolution. | en_US |