Evaluation of thin-slice abdominal DECT using deep-learning image reconstruction in 74 keV virtual monoenergetic images : an image quality comparison
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature..
PURPOSE: To compare noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR) and image quality using deep-learning image reconstruction (DLIR) vs. adaptive statistical iterative reconstruction (ASIR-V) in 0.625 and 2.5 mm slice thickness gray scale 74 keV virtual monoenergetic (VM) abdominal dual-energy CT (DECT).
METHODS: This retrospective study was approved by the institutional review board and regional ethics committee. We analysed 30 portal-venous phase abdominal fast kV-switching DECT (80/140kVp) scans. Data were reconstructed to ASIR-V 60% and DLIR-High at 74 keV in 0.625 and 2.5 mm slice thickness. Quantitative HU and noise assessment were measured within liver, aorta, adipose tissue and muscle. Two board-certified radiologists evaluated image noise, sharpness, texture and overall quality based on a five-point Likert scale.
RESULTS: DLIR significantly reduced image noise and increased CNR as well as SNR compared to ASIR-V, when slice thickness was maintained (p < 0.001). Slightly higher noise of 5.5-16.2% was measured (p < 0.01) in liver, aorta and muscle tissue at 0.625 mm DLIR compared to 2.5 mm ASIR-V, while noise in adipose tissue was 4.3% lower with 0.625 mm DLIR compared to 2.5 mm ASIR-V (p = 0.08). Qualitative assessments demonstrated significantly improved image quality for DLIR particularly in 0.625 mm images.
CONCLUSIONS: DLIR significantly reduced image noise, increased CNR and SNR and improved image quality in 0.625 mm slice images, when compared to ASIR-V. DLIR may facilitate thinner image slice reconstructions for routine contrast-enhanced abdominal DECT.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:48 |
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| Enthalten in: |
Abdominal radiology (New York) - 48(2023), 4 vom: 21. Apr., Seite 1536-1544 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xu, Jack J [VerfasserIn] |
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| Links: |
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| Themen: |
Computed tomography |
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| Anmerkungen: |
Date Completed 21.04.2023 Date Revised 12.06.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s00261-023-03845-w |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM353169978 |
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| 520 | |a © 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. | ||
| 520 | |a PURPOSE: To compare noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR) and image quality using deep-learning image reconstruction (DLIR) vs. adaptive statistical iterative reconstruction (ASIR-V) in 0.625 and 2.5 mm slice thickness gray scale 74 keV virtual monoenergetic (VM) abdominal dual-energy CT (DECT) | ||
| 520 | |a METHODS: This retrospective study was approved by the institutional review board and regional ethics committee. We analysed 30 portal-venous phase abdominal fast kV-switching DECT (80/140kVp) scans. Data were reconstructed to ASIR-V 60% and DLIR-High at 74 keV in 0.625 and 2.5 mm slice thickness. Quantitative HU and noise assessment were measured within liver, aorta, adipose tissue and muscle. Two board-certified radiologists evaluated image noise, sharpness, texture and overall quality based on a five-point Likert scale | ||
| 520 | |a RESULTS: DLIR significantly reduced image noise and increased CNR as well as SNR compared to ASIR-V, when slice thickness was maintained (p < 0.001). Slightly higher noise of 5.5-16.2% was measured (p < 0.01) in liver, aorta and muscle tissue at 0.625 mm DLIR compared to 2.5 mm ASIR-V, while noise in adipose tissue was 4.3% lower with 0.625 mm DLIR compared to 2.5 mm ASIR-V (p = 0.08). Qualitative assessments demonstrated significantly improved image quality for DLIR particularly in 0.625 mm images | ||
| 520 | |a CONCLUSIONS: DLIR significantly reduced image noise, increased CNR and SNR and improved image quality in 0.625 mm slice images, when compared to ASIR-V. DLIR may facilitate thinner image slice reconstructions for routine contrast-enhanced abdominal DECT | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Computed tomography | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Dual-energy CT | |
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| 700 | 1 | |a Ulriksen, Peter S |e verfasserin |4 aut | |
| 700 | 1 | |a Hansen, Kristoffer L |e verfasserin |4 aut | |
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