The Impact of Different Scanning Methods and Reconstruction Algorithms on CT Image Quality
NCT ID: NCT06142539
Last Updated: 2024-03-15
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
50 participants
OBSERVATIONAL
2024-03-13
2025-05-31
Brief Summary
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Methods: CT images of a phantom were reconstructed with Hybrid iterative reconstruction and deep learning image reconstruction (DLIR). The noise power spectrum (NPS) and task transfer function (TTF) were measured. Two patient groups were included in this study: consecutive patients who underwent unenhanced abdominal standard-dose CT reconstructed with hybrid iterative reconstruction (SDCT group) and consecutive patients who underwent unenhanced abdominal LDCT reconstructed of HIR and DLIR (LDCT group). The CT values, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the hepatic parenchyma and paraspinal muscle and abdominal subcutaneous fat were evaluated. Radiologists assessed the subjective image quality and lesion diagnostic confidence using a 5-point Likert scale. Quantitative and qualitative parameters were compared between SDCT and LDCT groups.
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Detailed Description
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Conditions
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Study Design
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OTHER
PROSPECTIVE
Study Groups
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SDCT group
No interventions assigned to this group
LDCT group
CT Radiation Doses
Obtaining Low CT Radiation Doses by Adjusting Dose Levels
Interventions
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CT Radiation Doses
Obtaining Low CT Radiation Doses by Adjusting Dose Levels
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
18 Years
100 Years
ALL
No
Sponsors
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Wei Li
OTHER
Responsible Party
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Wei Li
clinical professor
Locations
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uCT960+
Shandong, Jinan Shandong, China
Countries
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Central Contacts
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Facility Contacts
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References
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Qi H, Cui D, Xu S, Li W, Zeng Q. Image quality assessment of artificial intelligence iterative reconstruction for low dose unenhanced abdomen: comparison with hybrid iterative reconstruction. Abdom Radiol (NY). 2025 Jul;50(7):3353-3362. doi: 10.1007/s00261-024-04760-4. Epub 2024 Dec 21.
Other Identifiers
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WLi
Identifier Type: -
Identifier Source: org_study_id
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