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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Liver CT study group | 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Contrast-enhanced liver CT scan | Diagnostic Test | 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. |
| 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 |
| 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 |
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Exclusion Criteria:
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Patients with a suspicion of focal liver lesions had the plan to do a contrast-enhanced liver CT scan.
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| Name | Affiliation | Role |
|---|---|---|
| Jeong Min Lee, M.D. | Seoul National University Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Tubingen University Hospital | Tübingen | 72076 | Germany | |||
| Seoul National University Hospital |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38231025 | Derived | Lee DH, Lee JM, Lee CH, Afat S, Othman A. Image Quality and Diagnostic Performance of Low-Dose Liver CT with Deep Learning Reconstruction versus Standard-Dose CT. Radiol Artif Intell. 2024 Mar;6(2):e230192. doi: 10.1148/ryai.230192. |
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| ID | Term |
|---|---|
| D008113 | Liver Neoplasms |
| ID | Term |
|---|---|
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D004066 | Digestive System Diseases |
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|
| Seoul |
| 03080 |
| South Korea |
| Korea University Guro Hospital | Seoul | 08308 | South Korea |
| D008107 |
| Liver Diseases |