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  4. Bayesian reconstruction algorithms for low-dose computed tomography are not yet suitable in clinical context
 
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Bayesian reconstruction algorithms for low-dose computed tomography are not yet suitable in clinical context

Citation Link: https://doi.org/10.15480/882.8864
Publikationstyp
Journal Article
Date Issued
2023-08-23
Sprache
English
Author(s)
Kniep, Inga  
Mieling, Till Robin  
Medizintechnische und Intelligente Systeme E-1  
Gerling, Moritz
Schlaefer, Alexander  
Medizintechnische und Intelligente Systeme E-1  
Heinemann, Axel  
Ondruschka, Benjamin  
Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, Hamburg
TORE-DOI
10.15480/882.8864
TORE-URI
https://hdl.handle.net/11420/44315
Journal
Journal of Imaging  
Volume
9
Issue
9
Article Number
170
Citation
Journal of Imaging 9 (9): 170 (2023-8-23)
Publisher DOI
10.3390/jimaging9090170
Scopus ID
2-s2.0-85172087939
Publisher
MDPI
Computed tomography (CT) is a widely used examination technique that usually requires a compromise between image quality and radiation exposure. Reconstruction algorithms aim to reduce radiation exposure while maintaining comparable image quality. Recently, unsupervised deep learning methods have been proposed for this purpose. In this study, a promising sparse-view reconstruction method (posterior temperature optimized Bayesian inverse model; POTOBIM) is tested for its clinical applicability. For this study, 17 whole-body CTs of deceased were performed. In addition to POTOBIM, reconstruction was performed using filtered back projection (FBP). An evaluation was conducted by simulating sinograms and comparing the reconstruction with the original CT slice for each case. A quantitative analysis was performed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The quality was assessed visually using a modified Ludewig’s scale. In the qualitative evaluation, POTOBIM was rated worse than the reference images in most cases. A partially equivalent image quality could only be achieved with 80 projections per rotation. Quantitatively, POTOBIM does not seem to benefit from more than 60 projections. Although deep learning methods seem suitable to produce better image quality, the investigated algorithm (POTOBIM) is not yet suitable for clinical routine.
Subjects
Bayesian deep learning
POTOBIM
radiation exposure
sparse-view CT
DDC Class
620: Engineering
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
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