Bayesian Reconstruction Algorithms for Low-Dose Computed Tomography Are Not Yet Suitable in Clinical Context
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 be...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/9/9/170 |
| _version_ | 1797579409142579200 |
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| author | Inga Kniep Robin Mieling Moritz Gerling Alexander Schlaefer Axel Heinemann Benjamin Ondruschka |
| author_facet | Inga Kniep Robin Mieling Moritz Gerling Alexander Schlaefer Axel Heinemann Benjamin Ondruschka |
| author_sort | Inga Kniep |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T22:36:45Z |
| format | Article |
| id | doaj.art-f21489bcaa144d10b8596d446877ccf7 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T22:36:45Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-f21489bcaa144d10b8596d446877ccf72023-11-19T11:24:32ZengMDPI AGJournal of Imaging2313-433X2023-08-019917010.3390/jimaging9090170Bayesian Reconstruction Algorithms for Low-Dose Computed Tomography Are Not Yet Suitable in Clinical ContextInga Kniep0Robin Mieling1Moritz Gerling2Alexander Schlaefer3Axel Heinemann4Benjamin Ondruschka5Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, GermanyInstitute for Medical Technology and Intelligent Systems, Hamburg University of Technology, 21073 Hamburg, GermanyInstitute of Legal Medicine, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, GermanyInstitute for Medical Technology and Intelligent Systems, Hamburg University of Technology, 21073 Hamburg, GermanyInstitute of Legal Medicine, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, GermanyInstitute of Legal Medicine, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, GermanyComputed 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.https://www.mdpi.com/2313-433X/9/9/170Bayesian deep learningradiation exposuresparse-view CTPOTOBIM |
| spellingShingle | Inga Kniep Robin Mieling Moritz Gerling Alexander Schlaefer Axel Heinemann Benjamin Ondruschka Bayesian Reconstruction Algorithms for Low-Dose Computed Tomography Are Not Yet Suitable in Clinical Context Journal of Imaging Bayesian deep learning radiation exposure sparse-view CT POTOBIM |
| title | Bayesian Reconstruction Algorithms for Low-Dose Computed Tomography Are Not Yet Suitable in Clinical Context |
| title_full | Bayesian Reconstruction Algorithms for Low-Dose Computed Tomography Are Not Yet Suitable in Clinical Context |
| title_fullStr | Bayesian Reconstruction Algorithms for Low-Dose Computed Tomography Are Not Yet Suitable in Clinical Context |
| title_full_unstemmed | Bayesian Reconstruction Algorithms for Low-Dose Computed Tomography Are Not Yet Suitable in Clinical Context |
| title_short | Bayesian Reconstruction Algorithms for Low-Dose Computed Tomography Are Not Yet Suitable in Clinical Context |
| title_sort | bayesian reconstruction algorithms for low dose computed tomography are not yet suitable in clinical context |
| topic | Bayesian deep learning radiation exposure sparse-view CT POTOBIM |
| url | https://www.mdpi.com/2313-433X/9/9/170 |
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