Deep learning-based and hybrid-type iterative reconstructions for CT: comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions.
Jpn J Radiol
; 39(2): 186-197, 2021 Feb.
Article
em En
| MEDLINE
| ID: mdl-33037956
ABSTRACT
PURPOSE:
To determine the image quality improvement including vascular structures using deep learning reconstruction (DLR) for ultra-high-resolution CT (UHR-CT) and area-detector CT (ADCT) compared to a commercially available hybrid-iterative reconstruction (IR) method. MATERIALS ANDMETHOD:
Thirty-two patients suspected of renal cell carcinoma underwent dynamic contrast-enhanced (CE) CT using UHR-CT or ADCT systems. CT value and contrast-to-noise ratio (CNR) on each CT dataset were assessed with region of interest (ROI) measurements. For qualitative assessment of improvement for vascular structure visualization, each artery was assessed using a 5-point scale. To determine the utility of DLR, CT values and CNRs were compared among all UHR-CT data by means of ANOVA followed by Bonferroni post hoc test, and same values on ADCT data were also compared between hybrid IR and DLR methods by paired t test.RESULTS:
For all arteries except the aorta, the CT value and CNR of the DLR method were significantly higher compared to those of the hybrid-type IR method in both CT systems reconstructed as 512 or 1024 matrixes (p < 0.05).CONCLUSION:
DLR has a higher potential to improve the image quality resulting in a more accurate evaluation for vascular structures than hybrid IR for both UHR-CT and ADCT.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Carcinoma de Células Renais
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Tomografia Computadorizada por Raios X
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Abdome
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Melhoria de Qualidade
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Aprendizado Profundo
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Rim
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Neoplasias Renais
Tipo de estudo:
Qualitative_research
Limite:
Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article