Liver CT Dose Reduction With Deep Learning Based Reconstruction

Completed

• Conditions: Liver Cancer; Radiation Exposure
• Interventions: Diagnostic Test: Contrast-enhanced liver CT scan

• Registration Number: NCT05804799
• Lead Sponsor: Seoul National University Hospital

Brief Summary

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.

Detailed Description

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.

Study Timeline

• Study Start: 2021-01-01
• Primary Completion: 2022-08-31
• Study Completion: 2022-12-31
• Study First Submitted: 2023-03-27
• Study First Submitted QC: 2023-03-27
• Status Verified: 2023-04
• Study First Posted: 2023-04-07
• Last Update Submitted: 2023-04-10
• Last Update Posted: 2023-04-12

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 300

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Liver CT study group	Contrast-enhanced liver CT scan	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.

Primary Outcome Measures

Name	Time	Method
Measurement of standard deviation of CT attenuation values at the liver	within 6 months from acquisition of liver CT scans	Standard deviation of CT attenuation values at the liver parenchyma

Secondary Outcome Measures

Name	Time	Method
Sensitivity to detect malignant liver tumor	within 6 months from acquisition of liver CT scans	Sensitivity of liver CT scans to detect malignant liver tumor

Trial Locations

Locations (3)

Korea University Guro Hospital; 🇰🇷 Seoul, Korea, Republic of

Tubingen University Hospital; 🇩🇪 Tubingen, Germany

Seoul National University Hospital; 🇰🇷 Seoul, Korea, Republic of