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Liver CT Dose Reduction With Deep Learning Based Reconstruction

NCT05804799 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 300

Last updated 2023-04-12

No results posted yet for this study

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.

Conditions

Interventions

DIAGNOSTIC_TEST

Contrast-enhanced liver CT scan

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.

Sponsors & Collaborators

  • Seoul National University Hospital

    lead OTHER

Principal Investigators

  • Jeong Min Lee, M.D. · Seoul National University Hospital

Eligibility

Min Age
20 Years
Max Age
85 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-01-01
Primary Completion
2022-08-31
Completion
2022-12-31
FDA Device
Yes

Countries

  • Germany
  • South Korea

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05804799 on ClinicalTrials.gov