An adaptive smoothness regularization algorithm for optical tomography

In diffuse optical tomography (DOT), the object with unknown optical properties is illuminated with near infrared light and the absorption and diffusion coefficient distributions of a body are estimated from the scattering and transmission data. The problem is notoriously ill-posed and complementary information concerning the optical properties needs to be used to counter-effect the ill-posedness. In this article, we propose an adaptive inhomogenous anisotropic smoothness regularization scheme that corresponds to the prior information that the unknown object has a blocky structure. The algorithm updates alternatingly the current estimate and the smoothness penalty functional, and it is demonstrated with simulated data that the algorithm is capable of locating well blocky inclusions. The dynamical range of the reconstruction is improved, compared to traditional smoothness regularization schemes, and the crosstalk between the diffusion and absorption images is clearly less. The algorithm is tested also with a three-dimensional phantom data.