Strategies of Deep Learning for Tomographic Reconstruction

Abstract

In this article, we introduce three different strategies of tomographic reconstruction based on deep learning. These algorithms are model-based learning for iterative optimization. We discuss the basic principles of developing these algorithms. The performance of them is analyzed and evaluated both on theory and simulation reconstruction. We developed open-source software to run these algorithms in the same framework. From the simulation results, all these deep learning algorithms showed improvements in reconstruction quality and accuracy where the strategy based on Generative Adversarial Networks showed the advantage especially.