Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy
Advances in image reconstruction are necessary to decrease radiation exposure from coronary CT angiography (CCTA) further, but iterative reconstruction has been shown to degrade image quality at high levels. Deep-learning image reconstruction (DLIR) offers unique opportunities to overcome these limi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Benz, Dominik C. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Umfang: |
8 |
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| Übergeordnetes Werk: |
Enthalten in: Periodicities in fair weather potential gradient data from multiple stations at different latitudes - Tacza, J. ELSEVIER, 2022, official journal of the Society of Cardiovascular Computed Tomography, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:14 ; year:2020 ; number:5 ; pages:444-451 ; extent:8 |
| Links: |
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| DOI / URN: |
10.1016/j.jcct.2020.01.002 |
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| 520 | |a Advances in image reconstruction are necessary to decrease radiation exposure from coronary CT angiography (CCTA) further, but iterative reconstruction has been shown to degrade image quality at high levels. Deep-learning image reconstruction (DLIR) offers unique opportunities to overcome these limitations. The present study compared the impact of DLIR and adaptive statistical iterative reconstruction-Veo (ASiR-V) on quantitative and qualitative image parameters and the diagnostic accuracy of CCTA using invasive coronary angiography (ICA) as the standard of reference. | ||
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10.1016/j.jcct.2020.01.002 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001137.pica (DE-627)ELV05143055X (ELSEVIER)S1934-5925(19)30464-2 DE-627 ger DE-627 rakwb eng 550 530 VZ 38.81 bkl Benz, Dominik C. verfasserin aut Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy 2020 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Advances in image reconstruction are necessary to decrease radiation exposure from coronary CT angiography (CCTA) further, but iterative reconstruction has been shown to degrade image quality at high levels. Deep-learning image reconstruction (DLIR) offers unique opportunities to overcome these limitations. The present study compared the impact of DLIR and adaptive statistical iterative reconstruction-Veo (ASiR-V) on quantitative and qualitative image parameters and the diagnostic accuracy of CCTA using invasive coronary angiography (ICA) as the standard of reference. Image quality Elsevier DLIR Elsevier Diagnostic accuracy Elsevier Adaptive statistical iterative reconstruction-veo Elsevier ASiR-V Elsevier Coronary CT angiography Elsevier Deep-learning image reconstruction Elsevier Benetos, Georgios oth Rampidis, Georgios oth von Felten, Elia oth Bakula, Adam oth Sustar, Aleksandra oth Kudura, Ken oth Messerli, Michael oth Fuchs, Tobias A. oth Gebhard, Catherine oth Pazhenkottil, Aju P. oth Kaufmann, Philipp A. oth Buechel, Ronny R. oth Enthalten in Elsevier Tacza, J. ELSEVIER Periodicities in fair weather potential gradient data from multiple stations at different latitudes 2022 official journal of the Society of Cardiovascular Computed Tomography Amsterdam [u.a.] (DE-627)ELV008050279 volume:14 year:2020 number:5 pages:444-451 extent:8 https://doi.org/10.1016/j.jcct.2020.01.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.81 Atmosphäre VZ AR 14 2020 5 444-451 8 |
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Periodicities in fair weather potential gradient data from multiple stations at different latitudes |
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Periodicities in fair weather potential gradient data from multiple stations at different latitudes |
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Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy |
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Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy |
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Benz, Dominik C. |
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Periodicities in fair weather potential gradient data from multiple stations at different latitudes |
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Periodicities in fair weather potential gradient data from multiple stations at different latitudes |
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Benz, Dominik C. |
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validation of deep-learning image reconstruction for coronary computed tomography angiography: impact on noise, image quality and diagnostic accuracy |
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Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy |
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Advances in image reconstruction are necessary to decrease radiation exposure from coronary CT angiography (CCTA) further, but iterative reconstruction has been shown to degrade image quality at high levels. Deep-learning image reconstruction (DLIR) offers unique opportunities to overcome these limitations. The present study compared the impact of DLIR and adaptive statistical iterative reconstruction-Veo (ASiR-V) on quantitative and qualitative image parameters and the diagnostic accuracy of CCTA using invasive coronary angiography (ICA) as the standard of reference. |
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Advances in image reconstruction are necessary to decrease radiation exposure from coronary CT angiography (CCTA) further, but iterative reconstruction has been shown to degrade image quality at high levels. Deep-learning image reconstruction (DLIR) offers unique opportunities to overcome these limitations. The present study compared the impact of DLIR and adaptive statistical iterative reconstruction-Veo (ASiR-V) on quantitative and qualitative image parameters and the diagnostic accuracy of CCTA using invasive coronary angiography (ICA) as the standard of reference. |
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Advances in image reconstruction are necessary to decrease radiation exposure from coronary CT angiography (CCTA) further, but iterative reconstruction has been shown to degrade image quality at high levels. Deep-learning image reconstruction (DLIR) offers unique opportunities to overcome these limitations. The present study compared the impact of DLIR and adaptive statistical iterative reconstruction-Veo (ASiR-V) on quantitative and qualitative image parameters and the diagnostic accuracy of CCTA using invasive coronary angiography (ICA) as the standard of reference. |
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Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy |
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Benetos, Georgios Rampidis, Georgios von Felten, Elia Bakula, Adam Sustar, Aleksandra Kudura, Ken Messerli, Michael Fuchs, Tobias A. Gebhard, Catherine Pazhenkottil, Aju P. Kaufmann, Philipp A. Buechel, Ronny R. |
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Benetos, Georgios Rampidis, Georgios von Felten, Elia Bakula, Adam Sustar, Aleksandra Kudura, Ken Messerli, Michael Fuchs, Tobias A. Gebhard, Catherine Pazhenkottil, Aju P. Kaufmann, Philipp A. Buechel, Ronny R. |
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