Automated Objective Optimization of Iterative Image Reconstruction Protocols

Ziel/Aim The image quality achieved in iterative PET image reconstruction is influenced by several internal and user-settable parameters (number of iterations and subsets, PSF model, etc.). Typically, there are more than 3 user-settable parameters involved, interacting in a non-intuitive way. Reasonable settings typically are obtained interactively by try-and-error which is highly subjective. This proof-of-concept work proposes a method for automated reconstruction parameters optimization for a given, preselected image quality metric.