Liver CT Dose Reduction With Deep Learning Based Reconstruction

Study Overview
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Brief Summary
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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
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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.

Official Title
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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

Conditions
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Radiation Exposure, Liver Cancer

Intervention / Treatment
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* Diagnostic Test: Contrast-enhanced liver CT scan


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 20 Years
Maximum Age: 85 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Liver CT study group<br>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. | Diagnostic Test: Contrast-enhanced liver CT scan<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Measurement of standard deviation of CT attenuation values at the liver | Standard deviation of CT attenuation values at the liver parenchyma | within 6 months from acquisition of liver CT scans | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Sensitivity to detect malignant liver tumor | Sensitivity of liver CT scans to detect malignant liver tumor | within 6 months from acquisition of liver CT scans |