Abdominopelvic CT Image Quality : Evaluation of Thin (0.5-mm) Slices Using Deep Learning Reconstruction
BACKGROUND. Because thick-section images (typically 3-5 mm) have low image noise, radiologists typically use them to perform clinical interpretation, although they may additionally refer to thin-section images (typically 0.5-0.625 mm) for problem solving. Deep learning reconstruction (DLR) can yield thin-section images with low noise. OBJECTIVE. The purpose of this study is to compare abdominopelvic CT image quality between thin-section DLR images and thin- and thick-section hybrid iterative reconstruction (HIR) images. METHODS. This retrospective study included 50 patients (31 men and 19 women; median age, 64 years) who underwent abdominopelvic CT between June 15, 2020, and July 29, 2020. Images were reconstructed at 0.5-mm section using DLR and at 0.5-mm and 3.0-mm sections using HIR. Five radiologists independently performed pairwise comparisons (0.5-mm DLR and either 0.5-mm or 3.0-mm HIR) and recorded the preferred image for subjective image quality measures (scale, -2 to 2). The pooled scores of readers were compared with a score of 0 (denoting no preference). Image noise was quantified using the SD of ROIs on regions of homogeneous liver. RESULTS. For comparison of 0.5-mm DLR images and 0.5-mm HIR images, the median pooled score was 2 (indicating a definite preference for DLR) for noise and overall image quality and 1 (denoting a slight preference for DLR) for sharpness and natural appearance. For comparison of 0.5-mm DLR and 3.0-mm HIR, the median pooled score was 1 for the four previously mentioned measures. These assessments were all significantly different (p < .001) from 0. For artifacts, the median pooled score for both comparisons was 0, which was not significant for comparison with 3.0-mm HIR (p = .03) but was significant for comparison with 0.5-mm HIR (p < .001) due to imbalance in scores of 1 (n = 28) and -1 (slight preference for HIR, n = 1). Noise for 0.5-mm DLR was lower by mean differences of 12.8 HU compared with 0.5-mm HIR and 4.4 HU compared with 3.0-mm HIR (both p < .001). CONCLUSION. Thin-section DLR improves subjective image quality and reduces image noise compared with currently used thin- and thick-section HIR, without causing additional artifacts. CLINICAL IMPACT. Although further diagnostic performance studies are warranted, the findings suggest the possibility of replacing current use of both thin- and thick-section HIR with the use of thin-section DLR only during clinical interpretations.
| Errataetall: |
CommentIn: AJR Am J Roentgenol. 2022 Oct 26;:. - PMID 36287626 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:220 |
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| Enthalten in: |
AJR. American journal of roentgenology - 220(2023), 3 vom: 02. März, Seite 381-388 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Oostveen, Luuk J [VerfasserIn] |
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Date Completed 24.02.2023 Date Revised 27.02.2023 published: Print-Electronic CommentIn: AJR Am J Roentgenol. 2022 Oct 26;:. - PMID 36287626 Citation Status MEDLINE |
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| doi: |
10.2214/AJR.22.28319 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM347743412 |
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| 100 | 1 | |a Oostveen, Luuk J |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Abdominopelvic CT Image Quality |b Evaluation of Thin (0.5-mm) Slices Using Deep Learning Reconstruction |
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| 500 | |a CommentIn: AJR Am J Roentgenol. 2022 Oct 26;:. - PMID 36287626 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a BACKGROUND. Because thick-section images (typically 3-5 mm) have low image noise, radiologists typically use them to perform clinical interpretation, although they may additionally refer to thin-section images (typically 0.5-0.625 mm) for problem solving. Deep learning reconstruction (DLR) can yield thin-section images with low noise. OBJECTIVE. The purpose of this study is to compare abdominopelvic CT image quality between thin-section DLR images and thin- and thick-section hybrid iterative reconstruction (HIR) images. METHODS. This retrospective study included 50 patients (31 men and 19 women; median age, 64 years) who underwent abdominopelvic CT between June 15, 2020, and July 29, 2020. Images were reconstructed at 0.5-mm section using DLR and at 0.5-mm and 3.0-mm sections using HIR. Five radiologists independently performed pairwise comparisons (0.5-mm DLR and either 0.5-mm or 3.0-mm HIR) and recorded the preferred image for subjective image quality measures (scale, -2 to 2). The pooled scores of readers were compared with a score of 0 (denoting no preference). Image noise was quantified using the SD of ROIs on regions of homogeneous liver. RESULTS. For comparison of 0.5-mm DLR images and 0.5-mm HIR images, the median pooled score was 2 (indicating a definite preference for DLR) for noise and overall image quality and 1 (denoting a slight preference for DLR) for sharpness and natural appearance. For comparison of 0.5-mm DLR and 3.0-mm HIR, the median pooled score was 1 for the four previously mentioned measures. These assessments were all significantly different (p < .001) from 0. For artifacts, the median pooled score for both comparisons was 0, which was not significant for comparison with 3.0-mm HIR (p = .03) but was significant for comparison with 0.5-mm HIR (p < .001) due to imbalance in scores of 1 (n = 28) and -1 (slight preference for HIR, n = 1). Noise for 0.5-mm DLR was lower by mean differences of 12.8 HU compared with 0.5-mm HIR and 4.4 HU compared with 3.0-mm HIR (both p < .001). CONCLUSION. Thin-section DLR improves subjective image quality and reduces image noise compared with currently used thin- and thick-section HIR, without causing additional artifacts. CLINICAL IMPACT. Although further diagnostic performance studies are warranted, the findings suggest the possibility of replacing current use of both thin- and thick-section HIR with the use of thin-section DLR only during clinical interpretations | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a CT | |
| 650 | 4 | |a abdomen | |
| 650 | 4 | |a deep learning reconstruction | |
| 700 | 1 | |a Smit, Ewoud J |e verfasserin |4 aut | |
| 700 | 1 | |a Dekker, Helena M |e verfasserin |4 aut | |
| 700 | 1 | |a Buckens, Constantinus F |e verfasserin |4 aut | |
| 700 | 1 | |a Pegge, Sjoert A H |e verfasserin |4 aut | |
| 700 | 1 | |a de Lange, Frank |e verfasserin |4 aut | |
| 700 | 1 | |a Sechopoulos, Ioannis |e verfasserin |4 aut | |
| 700 | 1 | |a Prokop, Mathias |e verfasserin |4 aut | |
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