Altmann, Sebastian ; Abello Mercado, Mario A. ; Ucar, Felix A. ; Kronfeld, Andrea ; Al-Nawas, Bilal ; Mukhopadhyay, Anirban ; Booz, Christian ; Brockmann, Marc A. ; Othman, Ahmed E. (2023)
Ultra-High-Resolution CT of the Head and Neck with Deep Learning Reconstruction—Assessment of Image Quality and Radiation Exposure and Intraindividual Comparison with Normal-Resolution CT.
In: Diagnostics, 13 (9)
doi: 10.3390/diagnostics13091534
Article, Bibliographie
This is the latest version of this item.
Abstract
Objectives: To assess the benefits of ultra-high-resolution CT (UHR-CT) with deep learning– based image reconstruction engine (AiCE) regarding image quality and radiation dose and intraindividually compare it to normal-resolution CT (NR-CT). Methods: Forty consecutive patients with head and neck UHR-CT with AiCE for diagnosed head and neck malignancies and available prior NR-CT of a different scanner were retrospectively evaluated. Two readers evaluated subjective image quality using a 5-point Likert scale regarding image noise, image sharpness, artifacts, diagnostic acceptability, and assessability of various anatomic regions. For reproducibility, inter-reader agreement was analyzed. Furthermore, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and slope of the gray-value transition between different tissues were calculated. Radiation dose was evaluated by comparing CTDIvol, DLP, and mean effective dose values. Results: UHR-CT with AiCE reconstruction led to significant improvement in subjective (image noise and diagnostic acceptability: p < 0.000; ICC _ 0.91) and objective image quality (SNR: p < 0.000; CNR: p < 0.025) at significantly lower radiation doses (NR-CT 2.03 _ 0.14 mSv; UHR-CT 1.45 _ 0.11 mSv; p < 0.0001) compared to NR-CT. Conclusions: Compared to NR-CT, UHR-CT combined with AiCE provides superior image quality at a markedly lower radiation dose. With improved soft tissue assessment and potentially improved tumor detection, UHR-CT may add further value to the role of CT in the assessment of head and neck pathologies.
| Item Type: | Article |
|---|---|
| Erschienen: | 2023 |
| Creators: | Altmann, Sebastian ; Abello Mercado, Mario A. ; Ucar, Felix A. ; Kronfeld, Andrea ; Al-Nawas, Bilal ; Mukhopadhyay, Anirban ; Booz, Christian ; Brockmann, Marc A. ; Othman, Ahmed E. |
| Type of entry: | Bibliographie |
| Title: | Ultra-High-Resolution CT of the Head and Neck with Deep Learning Reconstruction—Assessment of Image Quality and Radiation Exposure and Intraindividual Comparison with Normal-Resolution CT |
| Language: | English |
| Date: | 24 April 2023 |
| Publisher: | MDPI |
| Journal or Publication Title: | Diagnostics |
| Volume of the journal: | 13 |
| Issue Number: | 9 |
| DOI: | 10.3390/diagnostics13091534 |
| Corresponding Links: | |
| Abstract: | Objectives: To assess the benefits of ultra-high-resolution CT (UHR-CT) with deep learning– based image reconstruction engine (AiCE) regarding image quality and radiation dose and intraindividually compare it to normal-resolution CT (NR-CT). Methods: Forty consecutive patients with head and neck UHR-CT with AiCE for diagnosed head and neck malignancies and available prior NR-CT of a different scanner were retrospectively evaluated. Two readers evaluated subjective image quality using a 5-point Likert scale regarding image noise, image sharpness, artifacts, diagnostic acceptability, and assessability of various anatomic regions. For reproducibility, inter-reader agreement was analyzed. Furthermore, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and slope of the gray-value transition between different tissues were calculated. Radiation dose was evaluated by comparing CTDIvol, DLP, and mean effective dose values. Results: UHR-CT with AiCE reconstruction led to significant improvement in subjective (image noise and diagnostic acceptability: p < 0.000; ICC _ 0.91) and objective image quality (SNR: p < 0.000; CNR: p < 0.025) at significantly lower radiation doses (NR-CT 2.03 _ 0.14 mSv; UHR-CT 1.45 _ 0.11 mSv; p < 0.0001) compared to NR-CT. Conclusions: Compared to NR-CT, UHR-CT combined with AiCE provides superior image quality at a markedly lower radiation dose. With improved soft tissue assessment and potentially improved tumor detection, UHR-CT may add further value to the role of CT in the assessment of head and neck pathologies. |
| Uncontrolled Keywords: | computed tomography, head and neck neoplasms, ultra-high resolution, image quality, radiation dose, deep learning |
| Identification Number: | Artikel-ID: 1534 |
| Additional Information: | This article belongs to the Special Issue Advances in CT Images |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Interactive Graphics Systems |
| Date Deposited: | 27 Nov 2023 14:39 |
| Last Modified: | 01 Aug 2024 09:01 |
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| Suche nach Titel in: | TUfind oder in Google |
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Ultra-High-Resolution CT of the Head and Neck with Deep Learning Reconstruction — Assessment of Image Quality and Radiation Exposure and Intraindividual Comparison with Normal-Resolution CT. (deposited 12 May 2023 08:10)
- Ultra-High-Resolution CT of the Head and Neck with Deep Learning Reconstruction—Assessment of Image Quality and Radiation Exposure and Intraindividual Comparison with Normal-Resolution CT. (deposited 27 Nov 2023 14:39) [Currently Displayed]
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