{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["9(1)"],"submitter":["Allner S"],"pubmed_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"],"journal":["Scientific reports"],"pagination":["6016"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6461679"],"repository":["biostudies-literature"],"pubmed_title":["Metric-guided regularisation parameter selection for statistical iterative reconstruction in computed tomography."],"pmcid":["PMC6461679"],"pubmed_authors":["Fehringer A","Gustschin A","Noel PB","Pfeiffer F","Allner S"],"additional_accession":[]},"is_claimable":false,"name":"Metric-guided regularisation parameter selection for statistical iterative reconstruction in computed tomography.","description":"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","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 Apr","modification":"2025-04-06T02:02:25.609Z","creation":"2019-06-06T21:10:42Z"},"accession":"S-EPMC6461679","cross_references":{"pubmed":["30979911"],"doi":["10.1038/s41598-019-40837-7"]}}