Regularized nonlinear least squares methods for hit position reconstruction in small gamma cameras

In order to improve the performance of a gamma camera, it's fundamental to accurately reconstruct the photon hit position on the detector surface. In the last years, the increasing demand of small highly-efficient PET systems led to the development of new hit position estimation methods, with the purpose of improving the performances near the edges of the detector, where most of the information is typically lost. In this paper we apply iterative optimization methods, based on the regularization of the nonlinear least squares problem, to estimate the photon hit position. Numerical results show that, compared with the classic Anger algorithm, the proposed methods allow to recover more information near the edges.