Three-dimensional optical image reconstruction from phantom and clinical data

A three-dimensional (3D) finite-element optical tomographic reconstruction algorithm based on a diffusion equation approximation is presented. The algorithm uses a regularized Newton method to update an initial (guess) optical property distribution iteratively in order to minimize an object function composed of a weighted sum of the squared difference between computed and measured data, and reconstructs the spatial distribution of the absorption and scattering coefficients of turbid media/tissues using DC data. Considering the memory requirements and computational cost for 3D reconstruction, the algorithm has been parallelized with Message-Passing Interface. We have conducted both phantom and in vivo clinical experiments to evaluate our parallelized three-dimensional reconstruction algorithm. The results show that 3D volumetric images of turbid media and in vivo tissues can be successfully reconstructed.