Liver Cancer, Radiation Exposure
Conditions
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.
Interventions
DIAGNOSTIC_TESTContrast-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
Seoul National University Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
20 Years to 85 Years
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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 |
Countries
Germany, South Korea
Outcome results
None listed