JPRN-UMIN000053064尚未招募Unknown
Combining deep learning reconstruction and compressive sensing investigation of the usefulness of high-speed coronary MRA imaging method - A study of coronary MRA high-speed imaging method using AI and CS in combination
适应症
试验速览
- 阶段
- Unknown
- 状态
- 尚未招募
- 入组人数
- 40
研究概览
简要总结
暂无简介。
研究设计
- 研究类型
- Interventional
入排标准
- 年龄范围
- 20years-old 至 ot applicable(—)
- 性别
- All
入选标准
- 未提供
排除标准
- •1) persons who could not give research consent 2) claustrophobic person
研究者
相似试验
招募中
不适用
Feasibility of deep learning reconstruction in abdominal CT: Optimization and validatioPatients with abnormal laboratory test results or upper abdominal symptoms that raised suspicions of abdominal malignancy underwent dynamic CT.JPRN-UMIN000037471Iwate Medical University300
招募中
1 期
Improving Brain and Chest Imaging Enhancing Clarity and Reducing Radiation with Deep Learning TechnologyCTRI/2023/07/055310Kasturba Medical College Manipal
已完成
Unknown
Deep learning using computed tomography to identify high-risk patients for acute small bowel obstructioDiseases of the digestive systemKCT0008330Ajou University600
招募中
不适用
Deep Learning Reconstruction Algorithms in Dual Low-dose CTADeep LearningNCT06372756Hao Tang1,200
已完成
不适用
Deep Learning Super Resolution Reconstruction for Fast and Motion Robust T2-weighted Prostate MRIMultiparametric MRIProstate CancerNCT05820113University Hospital, Bonn109
