In this talk, we use a linearization technique to transform the nonlinear matrix equation corresponding that models spectral computed tomography (CT) into an optimization problem that is based on a weighted least squares term and nonnegative bound constraints. To solve this optimization problem, we propose a new preconditioner that can reduce the condition number significantly, and with this preconditioner, we implement FISTA with projections to achieve remarkable improvements on convergence speed and image quality.

This presentation is part of Minisymposium “MS33 - Advances in reconstruction algorithms for computed tomography (4 parts)”
organized by: Gunay Dogan (Theiss Research, NIST) , Harbir Antil (George Mason University) , Elena Loli Piccolomini (Dept. Computer Science and Engineering, University of Bologna) , Samuli Siltanen (University of Helsinki) .

Authors:

Yunyi Hu (Emory University)

Martin Andersen (Technical University of Denmark)

James Nagy (Emory University)

Keywords:

computed tomography, image reconstruction, inverse problems, nonlinear optimization, numerical linear algebra