Deep Learning Image Reconstruction for Abdominal CT of Hepatocellular Carcinoma Compared With 3-TESLA MRI
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 50
- 试验地点
- 1
- 主要终点
- Lesion size in mm
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •undergoing CT and MRI scans in the same week, with protocols dedicated to the detection of HCC lesions
排除标准
- •imaging with radiological artefact
结局指标
主要结局
Lesion size in mm
时间窗: Through study completion, an average of 1 year
Hypervascular appearance of lesion
时间窗: Through study completion an average of 1 year
Qualitative measurement: presence or absence of hypervascular lesion
Hypervascular capsule
时间窗: Through study completion an average of 1 year
Qualitative measurement : presence or absence of hypervascular capsule
次要结局
- Infiltrative nature Classification of the hepatic lesion by LI-RADS in MRI(Through study completion an average of 1 year)
研究者
Valérie LAURENT
Principal Investigator
Central Hospital, Nancy, France