Iterative CT image reconstruction using neural network optimization algorithms

Stochastic or model-based iterative reconstruction is able to account for the stochastic nature of the CT imaging process and some artifacts and is able to provide better reconstruction quality. It is also, however, computationally expensive. In this work, we investigated the use of some of the neural network training algorithms such as momentum and Adam for iterative CT image reconstruction. Our experimental results indicate that these algorithms provide better results and faster convergence than basic gradient descent. They also provide competitive results to coordinate descent (a leading technique for iterative reconstruction) but, unlike coordinate descent, they can be implemented as parallel computations, hence can potentially accelerate iterative reconstruction in practice.