Abstract: Fan-to-Parallel Beam Conversion

In this paper, we derive a neural network architecture based on an analytical formulation of the parallel-to-fan beam conversion problem following the concept of precision learning [1]. Up to now, this precision learning approach was only used to augment networks with prior knowledge and or to add more flexibility into existing algorithms. We want to extent this approach: we demonstrate that we can drive a mathematical model to tackle a problem under consideration and use deep learning to formulate different hypothesis on efficient solution schemes that are then found as the point of optimality of a deep learning training process.