Liver CT Dose Reduction With Deep Learning Based Reconstruction

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.

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Key information

Conditions

Radiation Exposure Digestive System Diseases Digestive System Neoplasms Liver Cancer Liver Diseases Liver Neoplasms Neoplasms Neoplasms by Site

Age range

20 year–85 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Tubingen University Hospital, Tübingen, Germany

About this study

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.

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Age between 20-year-old and 85 years old
patients referred to the Radiology department to perform contrast-enhanced liver CT under the suspicion of focal liver lesions

Exclusion criteria

patients with estimated glomerular filtration rate < 60 mL/min/1.73m2
previous history of severe adverse reaction to iodinated contrast media.

Treatment and study plan

Contrast-enhanced liver CT scan

Diagnostic Test

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.

Primary outcomes

Measurement of standard deviation of CT attenuation values at the liver

Time frame: within 6 months from acquisition of liver CT scans

Standard deviation of CT attenuation values at the liver parenchyma

Secondary outcomes

Sensitivity to detect malignant liver tumor

Time frame: within 6 months from acquisition of liver CT scans

Sensitivity of liver CT scans to detect malignant liver tumor

Sponsors and collaborators

Lead sponsor

Seoul National University Hospital

Other

Registry information

Official study title

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

Important dates

Study start: 2021
Primary completion: 2022
Study completion: 2022
First posted: Apr 7, 2023
Registry last updated: Apr 12, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed