Cargando…
Metric-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 recon...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6461679/ https://www.ncbi.nlm.nih.gov/pubmed/30979911 http://dx.doi.org/10.1038/s41598-019-40837-7 |
_version_ | 1783410522620690432 |
---|---|
author | Allner, Sebastian Gustschin, Alex Fehringer, Andreas Noël, Peter B. Pfeiffer, Franz |
author_facet | Allner, Sebastian Gustschin, Alex Fehringer, Andreas Noël, Peter B. Pfeiffer, Franz |
author_sort | Allner, Sebastian |
collection | PubMed |
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 determination is a feedback loop approach we applied to numerically simulated FORBILD phantom data and verified with an experimental measurement of a micro-CT device. The outcome is evaluated visually and quantitatively by means of root mean squared error (RMSE) and structural similarity (SSIM) for the simulation and visually for the measured sample (no ground truth available). The final reconstructed images exhibit noise-suppressed iterative reconstruction. For both datasets, the optimisation is robust where its initial value is concerned. The parameter tuning approach shows that the proposed metric-driven feedback loop is a promising tool for finding a suitable regularisation parameter in statistical iterative reconstruction. |
format | Online Article Text |
id | pubmed-6461679 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-64616792019-04-17 Metric-guided regularisation parameter selection for statistical iterative reconstruction in computed tomography Allner, Sebastian Gustschin, Alex Fehringer, Andreas Noël, Peter B. Pfeiffer, Franz Sci Rep Article 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 determination is a feedback loop approach we applied to numerically simulated FORBILD phantom data and verified with an experimental measurement of a micro-CT device. The outcome is evaluated visually and quantitatively by means of root mean squared error (RMSE) and structural similarity (SSIM) for the simulation and visually for the measured sample (no ground truth available). The final reconstructed images exhibit noise-suppressed iterative reconstruction. For both datasets, the optimisation is robust where its initial value is concerned. The parameter tuning approach shows that the proposed metric-driven feedback loop is a promising tool for finding a suitable regularisation parameter in statistical iterative reconstruction. Nature Publishing Group UK 2019-04-12 /pmc/articles/PMC6461679/ /pubmed/30979911 http://dx.doi.org/10.1038/s41598-019-40837-7 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Allner, Sebastian Gustschin, Alex Fehringer, Andreas Noël, Peter B. Pfeiffer, Franz Metric-guided regularisation parameter selection for statistical iterative reconstruction in computed tomography |
title | Metric-guided regularisation parameter selection for statistical iterative reconstruction in computed tomography |
title_full | Metric-guided regularisation parameter selection for statistical iterative reconstruction in computed tomography |
title_fullStr | Metric-guided regularisation parameter selection for statistical iterative reconstruction in computed tomography |
title_full_unstemmed | Metric-guided regularisation parameter selection for statistical iterative reconstruction in computed tomography |
title_short | Metric-guided regularisation parameter selection for statistical iterative reconstruction in computed tomography |
title_sort | metric-guided regularisation parameter selection for statistical iterative reconstruction in computed tomography |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6461679/ https://www.ncbi.nlm.nih.gov/pubmed/30979911 http://dx.doi.org/10.1038/s41598-019-40837-7 |
work_keys_str_mv | AT allnersebastian metricguidedregularisationparameterselectionforstatisticaliterativereconstructionincomputedtomography AT gustschinalex metricguidedregularisationparameterselectionforstatisticaliterativereconstructionincomputedtomography AT fehringerandreas metricguidedregularisationparameterselectionforstatisticaliterativereconstructionincomputedtomography AT noelpeterb metricguidedregularisationparameterselectionforstatisticaliterativereconstructionincomputedtomography AT pfeifferfranz metricguidedregularisationparameterselectionforstatisticaliterativereconstructionincomputedtomography |