Metric-guided regularisation parameter selection for statistical iterative reconstruction in computed tomography.

ABSTRACT: As iterative reconstruction in Computed Tomography (CT) is an ill-posed problem, additional prior information has to be used to get a physically meaningful result (close to ground truth if available). However, the amount of influence of the regularisation prior is crucial to the outcome of the reconstruction. Therefore, we propose a scheme for tuning the strength of the prior via a certain image metric. In this work, the parameter is tuned for minimal histogram entropy in selected regions of the reconstruction as histogram entropy is a very basic approach to characterise the information content of data. We performed a sweep over different regularisation parameters showing that the histogram entropy is a suitable metric as it is well behaved over a wide range of parameters. The parameter de

SUBMITTER: Allner S 

PROVIDER: S-EPMC6461679 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature