Three-dimensional CT imaging in extensor tendons using deep learning reconstruction: optimal reconstruction parameters and the influence of dose
Abstract The purpose of this study was to assess the optimal reconstruction parameters and the influence of tube current in extensor tendons three-dimensional computed tomography (3D CT) using deep learning reconstruction, using iterative reconstruction as a reference. In the phantom study, a cylindrical phantom with a 3 mm rod simulated an extensor tendon was used. The phantom images were acquired at tube current of 50, 100, 150, 200, and 250 mA. In the clinical study, CT scans of hand tendons were performed on nine hands from eight patients. All images were reconstructed using advanced intelligent clear-IQ engine (AiCE) parameters (body, body sharp, brain CTA, and brain LCD) and adaptive iterative dose reduction three dimensional (AIDR 3D). The objective image quality for tendon detectability was evaluated by calculating the low-contrast object specific contrast-to-noise ratio ($ CNR_{LO} $) in the phantom study and CNR and coefficient of variation (CV) in the clinical study. In the phantom study, $ CNR_{LO} $ (at 200 mA) of AiCE parameters (body, body sharp, brain CTA, and brain LCD) and AIDR 3D were 5.2, 5.3, 5.3, 5.8, and 5.0, respectively. In the clinical study, AiCE brain CTA was higher CNR and lower CV values compared to other reconstruction parameters. AiCE without dose reduction may be an effective strategy for further improving the image quality of extensor tendons 3D CT. Our study suggests that the AiCE brain CTA is more suitable for extensor tendons 3D CT compared to other AiCE parameters..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:46 |
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| Enthalten in: |
Australasian physical & engineering sciences in medicine - 46(2023), 4 vom: 18. Sept., Seite 1659-1666 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Tsuboi, Kunihito [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Themen: |
Computed tomography |
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| Anmerkungen: |
© Australasian College of Physical Scientists and Engineers in Medicine 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| doi: |
10.1007/s13246-023-01326-4 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Abstract The purpose of this study was to assess the optimal reconstruction parameters and the influence of tube current in extensor tendons three-dimensional computed tomography (3D CT) using deep learning reconstruction, using iterative reconstruction as a reference. In the phantom study, a cylindrical phantom with a 3 mm rod simulated an extensor tendon was used. The phantom images were acquired at tube current of 50, 100, 150, 200, and 250 mA. In the clinical study, CT scans of hand tendons were performed on nine hands from eight patients. All images were reconstructed using advanced intelligent clear-IQ engine (AiCE) parameters (body, body sharp, brain CTA, and brain LCD) and adaptive iterative dose reduction three dimensional (AIDR 3D). The objective image quality for tendon detectability was evaluated by calculating the low-contrast object specific contrast-to-noise ratio ($ CNR_{LO} $) in the phantom study and CNR and coefficient of variation (CV) in the clinical study. In the phantom study, $ CNR_{LO} $ (at 200 mA) of AiCE parameters (body, body sharp, brain CTA, and brain LCD) and AIDR 3D were 5.2, 5.3, 5.3, 5.8, and 5.0, respectively. In the clinical study, AiCE brain CTA was higher CNR and lower CV values compared to other reconstruction parameters. AiCE without dose reduction may be an effective strategy for further improving the image quality of extensor tendons 3D CT. Our study suggests that the AiCE brain CTA is more suitable for extensor tendons 3D CT compared to other AiCE parameters. | ||
| 650 | 4 | |a Computed tomography |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Extensor tendon |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Three-dimensional image |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Deep learning reconstruction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Tube current |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Kanbe, Takamasa |4 aut | |
| 700 | 1 | |a Matsushima, Hiroshi |4 aut | |
| 700 | 1 | |a Ohtani, Yuki |4 aut | |
| 700 | 1 | |a Tanikawa, Ken |4 aut | |
| 700 | 1 | |a Kaneko, Masanori |4 aut | |
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