Towards deep iterative-reconstruction algorithms for computed tomography (CT) applications

We introduce a new approach for designing deep learning algorithms for computed tomography applications. Rather than training generically-structured neural network architectures to equivalently perform imaging tasks, we show how to leverage classical iterative-reconstruction algorithms such as Newton-Raphson and expectation-maximization (EM) to bootstrap network performance to a good initialization-point, with a well-understood baseline of performance. Specifically, we demonstrate a natural and systematic way to design these networks for both transmission-mode x-ray computed tomography (XRCT) and emission-mode single-photon computed tomography (SPECT), highlighting that our method is capable of preserving many of the nice properties, such as convergence and understandability, that is featured in classical approaches. The key contribution of this work is a formulation of the reconstruction task that enables data-driven improvements in image clarity and artifact reduction without sacrificing understandability. In this early work, we evaluate our method on a number of synthetic phantoms, highlighting some of the benefits and difficulties of this machine-learning approach.