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, 2023, 13 (9)
doi: 10.26083/tuprints-00023792
Artikel, Zweitveröffentlichung, Verlagsversion
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Altmann, Sebastian ; Abello Mercado, Mario A. ; Ucar, Felix A. ; Kronfeld, Andrea ; Al-Nawas, Bilal ; Mukhopadhyay, Anirban ; Booz, Christian ; Brockmann, Marc A. ; Othman, Ahmed E. |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | 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 |
| Sprache: | Englisch |
| Publikationsjahr: | 2023 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2023 |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Diagnostics |
| Jahrgang/Volume einer Zeitschrift: | 13 |
| (Heft-)Nummer: | 9 |
| Kollation: | 15 Seiten |
| DOI: | 10.26083/tuprints-00023792 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/23792 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | computed tomography, head and neck neoplasms, ultra-high resolution, image quality, radiation dose, deep learning |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-237921 |
| Zusätzliche Informationen: | This article belongs to the Special Issue Advances in CT Images |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin, Gesundheit |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Mai 2023 08:10 |
| Letzte Änderung: | 06 Jun 2023 09:09 |
| PPN: | |
| Zugehörige Links: | |
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| Suche nach Titel in: | TUfind oder in Google |
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