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Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest

OBJECTIVES: A deep learning-based super-resolution for postcontrast volume-interpolated breath-hold examination (VIBE) of the chest was investigated in this study. Aim was to improve image quality, noise, artifacts and diagnostic confidence without change of acquisition parameters. MATERIALS AND MET...

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Autores principales: Maennlin, Simon, Wessling, Daniel, Herrmann, Judith, Almansour, Haidara, Nickel, Dominik, Kannengiesser, Stephan, Afat, Saif, Gassenmaier, Sebastian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Milan 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9938811/
https://www.ncbi.nlm.nih.gov/pubmed/36609662
http://dx.doi.org/10.1007/s11547-022-01587-1
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author Maennlin, Simon
Wessling, Daniel
Herrmann, Judith
Almansour, Haidara
Nickel, Dominik
Kannengiesser, Stephan
Afat, Saif
Gassenmaier, Sebastian
author_facet Maennlin, Simon
Wessling, Daniel
Herrmann, Judith
Almansour, Haidara
Nickel, Dominik
Kannengiesser, Stephan
Afat, Saif
Gassenmaier, Sebastian
author_sort Maennlin, Simon
collection PubMed
description OBJECTIVES: A deep learning-based super-resolution for postcontrast volume-interpolated breath-hold examination (VIBE) of the chest was investigated in this study. Aim was to improve image quality, noise, artifacts and diagnostic confidence without change of acquisition parameters. MATERIALS AND METHODS: Fifty patients who received VIBE postcontrast imaging of the chest at 1.5 T were included in this retrospective study. After acquisition of the standard VIBE (VIBE(S)), a novel deep learning-based algorithm and a denoising algorithm were applied, resulting in enhanced images (VIBE(DL)). Two radiologists qualitatively evaluated both datasets independently, rating sharpness of soft tissue, vessels, bronchial structures, lymph nodes, artifacts, cardiac motion artifacts, noise levels and overall diagnostic confidence, using a Likert scale ranging from 1 to 4. In the presence of lung lesions, the largest lesion was rated regarding sharpness and diagnostic confidence using the same Likert scale as mentioned above. Additionally, the largest diameter of the lesion was measured. RESULTS: The sharpness of soft tissue, vessels, bronchial structures and lymph nodes as well as the diagnostic confidence, the extent of artifacts, the extent of cardiac motion artifacts and noise levels were rated superior in VIBE(DL) (all P < 0.001). There was no significant difference in the diameter or the localization of the largest lung lesion in VIBE(DL) compared to VIBE(S). Lesion sharpness as well as detectability was rated significantly better by both readers with VIBE(DL) (both P < 0.001). CONCLUSION: The application of a novel deep learning-based super-resolution approach in T1-weighted VIBE postcontrast imaging resulted in an improvement in image quality, noise levels and diagnostic confidence as well as in a shortened acquisition time.
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spelling pubmed-99388112023-02-20 Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest Maennlin, Simon Wessling, Daniel Herrmann, Judith Almansour, Haidara Nickel, Dominik Kannengiesser, Stephan Afat, Saif Gassenmaier, Sebastian Radiol Med Chest Radiology OBJECTIVES: A deep learning-based super-resolution for postcontrast volume-interpolated breath-hold examination (VIBE) of the chest was investigated in this study. Aim was to improve image quality, noise, artifacts and diagnostic confidence without change of acquisition parameters. MATERIALS AND METHODS: Fifty patients who received VIBE postcontrast imaging of the chest at 1.5 T were included in this retrospective study. After acquisition of the standard VIBE (VIBE(S)), a novel deep learning-based algorithm and a denoising algorithm were applied, resulting in enhanced images (VIBE(DL)). Two radiologists qualitatively evaluated both datasets independently, rating sharpness of soft tissue, vessels, bronchial structures, lymph nodes, artifacts, cardiac motion artifacts, noise levels and overall diagnostic confidence, using a Likert scale ranging from 1 to 4. In the presence of lung lesions, the largest lesion was rated regarding sharpness and diagnostic confidence using the same Likert scale as mentioned above. Additionally, the largest diameter of the lesion was measured. RESULTS: The sharpness of soft tissue, vessels, bronchial structures and lymph nodes as well as the diagnostic confidence, the extent of artifacts, the extent of cardiac motion artifacts and noise levels were rated superior in VIBE(DL) (all P < 0.001). There was no significant difference in the diameter or the localization of the largest lung lesion in VIBE(DL) compared to VIBE(S). Lesion sharpness as well as detectability was rated significantly better by both readers with VIBE(DL) (both P < 0.001). CONCLUSION: The application of a novel deep learning-based super-resolution approach in T1-weighted VIBE postcontrast imaging resulted in an improvement in image quality, noise levels and diagnostic confidence as well as in a shortened acquisition time. Springer Milan 2023-01-07 2023 /pmc/articles/PMC9938811/ /pubmed/36609662 http://dx.doi.org/10.1007/s11547-022-01587-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Chest Radiology
Maennlin, Simon
Wessling, Daniel
Herrmann, Judith
Almansour, Haidara
Nickel, Dominik
Kannengiesser, Stephan
Afat, Saif
Gassenmaier, Sebastian
Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest
title Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest
title_full Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest
title_fullStr Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest
title_full_unstemmed Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest
title_short Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest
title_sort application of deep learning-based super-resolution to t1-weighted postcontrast gradient echo imaging of the chest
topic Chest Radiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9938811/
https://www.ncbi.nlm.nih.gov/pubmed/36609662
http://dx.doi.org/10.1007/s11547-022-01587-1
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