Iterative reconstruction of projection images from a microlens-based optical detector

Abstract

A microlens-based optical detector was developed to perform small animal optical imaging. In this paper we present an iterative reconstruction algorithm yielding improved image quality and spatial resolution as compared to conventional inverse mapping. The reconstruction method utilizes the compressive sensing concept to cope with the undersampling nature of the problem. Each iteration in the algorithm contains two separate steps to ensure both the convergence of the least-square solution and the minimization of the l(1)-norm of the sparsifying transform. The results estimated from measurements, employing a Derenzo-like pattern and a Siemens star phantom, illustrate significant improvements in contrast and spatial resolution in comparison to results calculated by inverse mapping.