Comparison of Image Quality and Diagnostic Pefromance of Low Dose Liver CT With Deep Learning Reconstuction to Standard Dose CT: A Prospective Multicenter Non-inferiority Trial
Sponsor study identifier
observational
300
2 countries
3
Study Start
January 1, 2021
Actual (reported)Primary Completion
August 31, 2022
Actual (reported)Study Completion
December 31, 2022
Actual (reported)A deep learning-based de-noising (DLD) reconstruction algorithm (ClariCT.AI) has the potential to reduce image noise and improve image quality. This capability of the CliriCT.AI program might enable dose reduction for contrast-enhanced liver CT examination. In this prospective multicenter study, whether the ClariCT.AI program can reduce the noise level of low-dose contrast-enhanced liver CT (LDCT) data and therefore, can provide comparable image quality to the standard dose of contrast-enhanced liver CT (SDCT) images will be evaluated. The aim of this study is to compare image quality and diagnostic capability in detecting malignant tumors of LDCT with DLD to those of SDCT with MBIR using the predefined non-inferiority margin.
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
December 31, 2022
Actual (reported)Click on a node to explore related trials.
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Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
January 1, 2021
Actual (reported)August 31, 2022
Actual (reported)December 31, 2022
Actual (reported)March 27, 2023
Actual (reported)April 7, 2023
Actual (reported)Measurement of standard deviation of CT attenuation values at the liver
Standard deviation of CT attenuation values at the liver parenchyma
within 6 months from acquisition of liver CT scans
Sensitivity to detect malignant liver tumor
within 6 months from acquisition of liver CT scans
Patients with a suspicion of focal liver lesions had the plan to perform a contrast-enhanced liver CT scan. The liver CT images were reconstructed by both low-dose scans with a deep-learning-based denoising program (ClariCT.AI) and standard-dose scans with model-based iterative reconstruction.
The contrast-enhanced liver CT scans were obtained from all of the participants. The liver CT images were reconstructed by both low-dose scans with a deep-learning-based denoising program (ClariCT.AI) and standard-dose scans with model-based iterative reconstruction.
Registry-reported names are shown in full. Names without a link have not yet been matched to a Syfrah profile.
Patients with a suspicion of focal liver lesions had the plan to do a contrast-enhanced liver CT scan.
Tubingen University Hospital
Tübingen, 72076, Germany
Seoul National University Hospital
Seoul, 03080, South Korea
Korea University Guro Hospital
Seoul, 08308, South Korea
Lee DH, Lee JM, Lee CH, Afat S, Othman A. Image Quality and Diagnostic Performance of Low-Dose Liver CT with Deep Learning Reconstruction versus Standard-Dose CT. Radiol Artif Intell. 2024 Mar;6(2):e230192. doi: 10.1148/ryai.230192.
Jeong Min Lee, M.D.
Seoul National University Hospital
300 participants (actual enrollment reported by the registry).
First Submitted
March 27, 2023
First Posted
April 7, 2023
Study Start
January 1, 2021
Actual (reported)Impact of Gender on the Outcome of Liver Transplantation for Hepatocellular Carcinoma
Contact the study team to confirm eligibility.
Primary Completion
August 31, 2022
Actual (reported)Study Completion
December 31, 2022
Actual (reported)Last Updated
April 12, 2023
Record last verified: 2023-04
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Source: ClinicalTrials.gov.
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