跳至主要内容
临床试验/NCT03980470
NCT03980470已完成不适用

Usefulness of Deep-Learning Image Reconstruction for Cardiac Computed Tomography Angiography - a Prospective, Non-randomized Observational Trial

University of Zurich2 个研究点 分布在 1 个国家目标入组 50 人开始时间: 2019年5月8日最近更新:
适应症

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

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

申办方类型
Other
责任方
Principal Investigator
主要研究者

Ronny R Buechel, MD

PD Dr. med. Ronny R. Buechel

University of Zurich

研究点 (2)

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