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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

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-...

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Autores principales: Altmann, Sebastian, Abello Mercado, Mario A., Ucar, Felix A., Kronfeld, Andrea, Al-Nawas, Bilal, Mukhopadhyay, Anirban, Booz, Christian, Brockmann, Marc A., Othman, Ahmed E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10177822/
https://www.ncbi.nlm.nih.gov/pubmed/37174926
http://dx.doi.org/10.3390/diagnostics13091534
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author Altmann, Sebastian
Abello Mercado, Mario A.
Ucar, Felix A.
Kronfeld, Andrea
Al-Nawas, Bilal
Mukhopadhyay, Anirban
Booz, Christian
Brockmann, Marc A.
Othman, Ahmed E.
author_facet Altmann, Sebastian
Abello Mercado, Mario A.
Ucar, Felix A.
Kronfeld, Andrea
Al-Nawas, Bilal
Mukhopadhyay, Anirban
Booz, Christian
Brockmann, Marc A.
Othman, Ahmed E.
author_sort Altmann, Sebastian
collection PubMed
description 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.
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spelling pubmed-101778222023-05-13 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 Altmann, Sebastian Abello Mercado, Mario A. Ucar, Felix A. Kronfeld, Andrea Al-Nawas, Bilal Mukhopadhyay, Anirban Booz, Christian Brockmann, Marc A. Othman, Ahmed E. Diagnostics (Basel) Article 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. MDPI 2023-04-24 /pmc/articles/PMC10177822/ /pubmed/37174926 http://dx.doi.org/10.3390/diagnostics13091534 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Altmann, Sebastian
Abello Mercado, Mario A.
Ucar, Felix A.
Kronfeld, Andrea
Al-Nawas, Bilal
Mukhopadhyay, Anirban
Booz, Christian
Brockmann, Marc A.
Othman, Ahmed E.
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
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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10177822/
https://www.ncbi.nlm.nih.gov/pubmed/37174926
http://dx.doi.org/10.3390/diagnostics13091534
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