Usefulness of Deep-Learning Image Reconstruction for Cardiac Computed Tomography Angiography - a Prospective, Non-randomized Observational Trial
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 50
- 试验地点
- 2
- 主要终点
- Subjective Image Quality
研究概览
简要总结
Cardiac CT allows the assessment of the heart and of the coronary arteries by use of ionising radiation. Although radiation exposure was significantly reduced in recent years, further decrease in radiation exposure is limited by increased image noise and deterioration in image quality. Recent evidence suggests that further technological refinements with artificial intelligence allows improved post-processing of images with reduction of image noise.
The present study aims at assessing the potential of a deep-learning image reconstruction algorithm in a clinical setting. Specifically, after a standard clinical scan, patients are scanned with lower radiation exposure and reconstructed with the DLIR algorithm. This interventional scan is then compared to the standard clinical scan.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
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
- •Patients referred for cardiac CT angiography
- •Age ≥ 18 years
- •Written informed consent
排除标准
- •Pregnancy or breast-feeding
- •Enrollment of the investigator, his/her family members, employees and other dependent persons
- •Renal insufficiency (GFR below 35 mL/min/1.73 m²)
结局指标
主要结局
Subjective Image Quality
时间窗: Day 1
Subjective image quality as measured by Likert scale from 1 (non-evaluable) to 5 (excellent)
次要结局
- Signal-to-noise Ratio(Day 1)
- Signal Intensity(Day 1)
- Dose-length Products(Day 1)
- Image Noise(Day 1)
- Plaque Volumes(Day 1)
研究者
Ronny R Buechel, MD
PD Dr. med. Ronny R. Buechel
University of Zurich
