Deep Learning Image Reconstruction for Abdominal CT of Hepatocellular Carcinoma Compared With 3-TESLA MRI
- Conditions
- Hepatocellular Carcinoma
- Registration Number
- NCT06037343
- Lead Sponsor
- Central Hospital, Nancy, France
- Brief Summary
New algorithms for processing CT acquisitions, based on artificial intelligence, have been reported to improve acquisition quality. Thats' why it's possible to imagine that new scan post-processing algorithms enable better detection and characterization of hepatocellular carcinoma lesions than with standard reconstructions. DLIR reconstructions could even match with MRI detection.
The aim of the study is to compare the detection and characterization of hepatic lesions according to the LI-RADS classification in CT with DLIR artificial intelligence reconstruction, compared with ASIR-V reconstruction and the gold standard of MRI.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 50
- undergoing CT and MRI scans in the same week, with protocols dedicated to the detection of HCC lesions
- imaging with radiological artefact
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Lesion size in mm Through study completion, an average of 1 year Hypervascular appearance of lesion Through study completion an average of 1 year Qualitative measurement: presence or absence of hypervascular lesion
Hypervascular capsule Through study completion an average of 1 year Qualitative measurement : presence or absence of hypervascular capsule
- Secondary Outcome Measures
Name Time Method Infiltrative nature Classification of the hepatic lesion by LI-RADS in MRI Through study completion an average of 1 year
Trial Locations
- Locations (1)
CHRU de Nancy
🇫🇷Nancy, France