Liver CT Dose Reduction With Deep Learning Based Reconstruction

Status Completed

Clinical Trial 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.

Clinical Trial Description

Study Design

Related Conditions & MeSH terms

Clinical Trial Details

NCT number	NCT05804799
Study type	Observational [Patient Registry]
Source	Seoul National University Hospital
Contact
Status	Completed
Phase
Start date	January 1, 2021
Completion date	December 31, 2022

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