Use of a priori information and penalty terms in gradient-based iterative reconstruction schemes

It is well known that the reconstruction problem in optical tomography is ill-posed. Therefore, the choice of an appropriate regularization method is of crucial importance for any successful image reconstruction algorithm. In this work we approach the regularization problem within a gradient-based image iterative reconstruction (GIIR) scheme. The image reconstruction is considered as a minimization of an appropriately defined objective function. The objective function can be separated into a least-square-error term, which compares predicted and actual detector readings, and additional penalty terms that may contain additional a priori information about the system. For the efficient minimization of this objective function the gradient with respect to the spatial distribution of optical properties is calculated. Besides presenting the underlying concepts in our approach to the regularization problem, we will show numerical results that demonstrate how prior knowledge can improve the reconstruction results.