Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography
We propose a pipeline for synthetic generation of personalized Computer Tomography (CT) images, with a radiation exposure evaluation and a lifetime attributable risk (LAR) assessment. We perform a patient-specific performance evaluation for a broad range of denoising algorithms (including the most p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/8/6/156 |
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| author | Illia Horenko Lukáš Pospíšil Edoardo Vecchi Steffen Albrecht Alexander Gerber Beate Rehbock Albrecht Stroh Susanne Gerber |
| author_facet | Illia Horenko Lukáš Pospíšil Edoardo Vecchi Steffen Albrecht Alexander Gerber Beate Rehbock Albrecht Stroh Susanne Gerber |
| author_sort | Illia Horenko |
| collection | DOAJ |
| description | We propose a pipeline for synthetic generation of personalized Computer Tomography (CT) images, with a radiation exposure evaluation and a lifetime attributable risk (LAR) assessment. We perform a patient-specific performance evaluation for a broad range of denoising algorithms (including the most popular deep learning denoising approaches, wavelets-based methods, methods based on Mumford–Shah denoising, etc.), focusing both on accessing the capability to reduce the patient-specific CT-induced LAR and on computational cost scalability. We introduce a parallel Probabilistic Mumford–Shah denoising model (PMS) and show that it markedly-outperforms the compared common denoising methods in denoising quality and cost scaling. In particular, we show that it allows an approximately 22-fold robust patient-specific LAR reduction for infants and a 10-fold LAR reduction for adults. Using a normal laptop, the proposed algorithm for PMS allows cheap and robust (with a multiscale structural similarity index >90%) denoising of very large 2D videos and 3D images (with over <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mn>7</mn></msup></semantics></math></inline-formula> voxels) that are subject to ultra-strong noise (Gaussian and non-Gaussian) for signal-to-noise ratios far below 1.0. The code is provided for open access. |
| first_indexed | 2024-03-09T23:24:40Z |
| format | Article |
| id | doaj.art-a5d4a186d82d48bf86372e4930ed3b04 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-09T23:24:40Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-a5d4a186d82d48bf86372e4930ed3b042023-11-23T17:20:26ZengMDPI AGJournal of Imaging2313-433X2022-05-018615610.3390/jimaging8060156Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed TomographyIllia Horenko0Lukáš Pospíšil1Edoardo Vecchi2Steffen Albrecht3Alexander Gerber4Beate Rehbock5Albrecht Stroh6Susanne Gerber7Faculty of Mathematics, Technical University of Kaiserslautern, 67663 Kaiserslautern, GermanyDepartment of Mathematics, VSB Ostrava, Ludvika Podeste 1875/17, 708 33 Ostrava, Czech RepublicInstitute of Computing, Faculty of Informatics, Universitá della Svizzera Italiana (USI), 6962 Viganello, SwitzerlandInstitute of Physiology, University Medical Center of the Johannes Gutenberg—University Mainz, 55128 Mainz, GermanyInstitute of Occupational Medicine, Faculty of Medicine, GU Frankfurt, 60590 Frankfurt am Main, GermanyLung Radiology Center Berlin, 10627 Berlin, GermanyInstitute of Pathophysiology, University Medical Center of the Johannes Gutenberg—University Mainz, 55128 Mainz, GermanyInstitute for Human Genetics, University Medical Center of the Johannes Gutenberg—University Mainz, 55128 Mainz, GermanyWe propose a pipeline for synthetic generation of personalized Computer Tomography (CT) images, with a radiation exposure evaluation and a lifetime attributable risk (LAR) assessment. We perform a patient-specific performance evaluation for a broad range of denoising algorithms (including the most popular deep learning denoising approaches, wavelets-based methods, methods based on Mumford–Shah denoising, etc.), focusing both on accessing the capability to reduce the patient-specific CT-induced LAR and on computational cost scalability. We introduce a parallel Probabilistic Mumford–Shah denoising model (PMS) and show that it markedly-outperforms the compared common denoising methods in denoising quality and cost scaling. In particular, we show that it allows an approximately 22-fold robust patient-specific LAR reduction for infants and a 10-fold LAR reduction for adults. Using a normal laptop, the proposed algorithm for PMS allows cheap and robust (with a multiscale structural similarity index >90%) denoising of very large 2D videos and 3D images (with over <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mn>7</mn></msup></semantics></math></inline-formula> voxels) that are subject to ultra-strong noise (Gaussian and non-Gaussian) for signal-to-noise ratios far below 1.0. The code is provided for open access.https://www.mdpi.com/2313-433X/8/6/156denoisingnonparametric methodsMumford–Shah formalismLAR reduction |
| spellingShingle | Illia Horenko Lukáš Pospíšil Edoardo Vecchi Steffen Albrecht Alexander Gerber Beate Rehbock Albrecht Stroh Susanne Gerber Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography Journal of Imaging denoising nonparametric methods Mumford–Shah formalism LAR reduction |
| title | Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography |
| title_full | Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography |
| title_fullStr | Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography |
| title_full_unstemmed | Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography |
| title_short | Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography |
| title_sort | low cost probabilistic 3d denoising with applications for ultra low radiation computed tomography |
| topic | denoising nonparametric methods Mumford–Shah formalism LAR reduction |
| url | https://www.mdpi.com/2313-433X/8/6/156 |
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