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Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography
OBJECTIVES: Deep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921160/ https://www.ncbi.nlm.nih.gov/pubmed/34792635 http://dx.doi.org/10.1007/s00330-021-08367-x |
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author | Benz, Dominik C. Ersözlü, Sara Mojon, François L. A. Messerli, Michael Mitulla, Anna K. Ciancone, Domenico Kenkel, David Schaab, Jan A. Gebhard, Catherine Pazhenkottil, Aju P. Kaufmann, Philipp A. Buechel, Ronny R. |
author_facet | Benz, Dominik C. Ersözlü, Sara Mojon, François L. A. Messerli, Michael Mitulla, Anna K. Ciancone, Domenico Kenkel, David Schaab, Jan A. Gebhard, Catherine Pazhenkottil, Aju P. Kaufmann, Philipp A. Buechel, Ronny R. |
author_sort | Benz, Dominik C. |
collection | PubMed |
description | OBJECTIVES: Deep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its impact on stenosis severity, plaque composition analysis, and plaque volume quantification. METHODS: This prospective study includes 50 patients who underwent two sequential CCTA scans at normal-dose (ND) and lower-dose (LD). ND scans were reconstructed with Adaptive Statistical Iterative Reconstruction-Veo (ASiR-V) 100%, and LD scans with DLIR. Image noise (in Hounsfield units, HU) and quantitative plaque volumes (in mm(3)) were assessed quantitatively. Stenosis severity was visually categorized into no stenosis (0%), stenosis (< 20%, 20–50%, 51–70%, 71–90%, 91–99%), and occlusion (100%). Plaque composition was classified as calcified, non-calcified, or mixed. RESULTS: Reduction of radiation dose from ND scans with ASiR-V 100% to LD scans with DLIR at the highest level (DLIR-H; 1.4 mSv vs. 0.8 mSv, p < 0.001) had no impact on image noise (28 vs. 27 HU, p = 0.598). Reliability of stenosis severity and plaque composition was excellent between ND scans with ASiR-V 100% and LD scans with DLIR-H (intraclass correlation coefficients of 0.995 and 0.974, respectively). Comparison of plaque volumes using Bland–Altman analysis revealed a mean difference of − 0.8 mm(3) (± 2.5 mm(3)) and limits of agreement between − 5.8 and + 4.1 mm(3). CONCLUSION: DLIR enables a reduction in radiation dose from CCTA by 43% without significant impact on image noise, stenosis severity, plaque composition, and quantitative plaque volume. KEY POINTS: •Deep-learning image reconstruction (DLIR) enables radiation dose reduction by over 40% for coronary computed tomography angiography (CCTA). •Image noise remains unchanged between a normal-dose CCTA reconstructed by ASiR-V and a lower-dose CCTA reconstructed by DLIR. •There is no impact on the assessment of stenosis severity, plaque composition, and quantitative plaque volume between the two scans. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-021-08367-x. |
format | Online Article Text |
id | pubmed-8921160 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-89211602022-03-17 Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography Benz, Dominik C. Ersözlü, Sara Mojon, François L. A. Messerli, Michael Mitulla, Anna K. Ciancone, Domenico Kenkel, David Schaab, Jan A. Gebhard, Catherine Pazhenkottil, Aju P. Kaufmann, Philipp A. Buechel, Ronny R. Eur Radiol Cardiac OBJECTIVES: Deep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its impact on stenosis severity, plaque composition analysis, and plaque volume quantification. METHODS: This prospective study includes 50 patients who underwent two sequential CCTA scans at normal-dose (ND) and lower-dose (LD). ND scans were reconstructed with Adaptive Statistical Iterative Reconstruction-Veo (ASiR-V) 100%, and LD scans with DLIR. Image noise (in Hounsfield units, HU) and quantitative plaque volumes (in mm(3)) were assessed quantitatively. Stenosis severity was visually categorized into no stenosis (0%), stenosis (< 20%, 20–50%, 51–70%, 71–90%, 91–99%), and occlusion (100%). Plaque composition was classified as calcified, non-calcified, or mixed. RESULTS: Reduction of radiation dose from ND scans with ASiR-V 100% to LD scans with DLIR at the highest level (DLIR-H; 1.4 mSv vs. 0.8 mSv, p < 0.001) had no impact on image noise (28 vs. 27 HU, p = 0.598). Reliability of stenosis severity and plaque composition was excellent between ND scans with ASiR-V 100% and LD scans with DLIR-H (intraclass correlation coefficients of 0.995 and 0.974, respectively). Comparison of plaque volumes using Bland–Altman analysis revealed a mean difference of − 0.8 mm(3) (± 2.5 mm(3)) and limits of agreement between − 5.8 and + 4.1 mm(3). CONCLUSION: DLIR enables a reduction in radiation dose from CCTA by 43% without significant impact on image noise, stenosis severity, plaque composition, and quantitative plaque volume. KEY POINTS: •Deep-learning image reconstruction (DLIR) enables radiation dose reduction by over 40% for coronary computed tomography angiography (CCTA). •Image noise remains unchanged between a normal-dose CCTA reconstructed by ASiR-V and a lower-dose CCTA reconstructed by DLIR. •There is no impact on the assessment of stenosis severity, plaque composition, and quantitative plaque volume between the two scans. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-021-08367-x. Springer Berlin Heidelberg 2021-11-18 2022 /pmc/articles/PMC8921160/ /pubmed/34792635 http://dx.doi.org/10.1007/s00330-021-08367-x Text en © The Author(s) 2021, corrected publication 2022 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 | Cardiac Benz, Dominik C. Ersözlü, Sara Mojon, François L. A. Messerli, Michael Mitulla, Anna K. Ciancone, Domenico Kenkel, David Schaab, Jan A. Gebhard, Catherine Pazhenkottil, Aju P. Kaufmann, Philipp A. Buechel, Ronny R. Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography |
title | Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography |
title_full | Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography |
title_fullStr | Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography |
title_full_unstemmed | Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography |
title_short | Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography |
title_sort | radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography |
topic | Cardiac |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921160/ https://www.ncbi.nlm.nih.gov/pubmed/34792635 http://dx.doi.org/10.1007/s00330-021-08367-x |
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