biostudies-literatureUnknown9(1)Allner SAs 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 deScientific reports6016https://www.ebi.ac.uk/biostudies/studies/S-EPMC6461679biostudies-literatureMetric-guided regularisation parameter selection for statistical iterative reconstruction in computed tomography.PMC6461679Fehringer AGustschin ANoel PBPfeiffer FAllner SfalseMetric-guided regularisation parameter selection for statistical iterative reconstruction in computed tomography.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 de2019-01-01T00:00:00Z2019 Apr2025-04-06T02:02:25.609Z2019-06-06T21:10:42ZS-EPMC64616793097991110.1038/s41598-019-40837-7