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OpenTrials
Completed

NCT Number: NCT03980470

Deep-Learning Image Reconstruction in CCTA

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.

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Key information

Conditions

Coronary Artery Disease Arterial Occlusive Diseases Arteriosclerosis Cardiovascular Diseases Coronary Disease Heart Diseases Myocardial Ischemia Vascular Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

University Hospital

Zurich, 8091, Switzerland

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Patients referred for cardiac CT angiography
  • Age ≥ 18 years
  • Written informed consent

Exclusion criteria

  • 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)

Treatment and study plan

TrueFidelity

Device

TrueFidelity (Deep Learning Image Reconstruction, DLIR) software by GE Healthcare.

The medical device in question is a novel reconstruction algorithm for raw CT data which is based on artificial intelligence approaches, namely deep-learning iterative reconstruction (DLIR). This DLIR algorithm will be installed on the console of the CT Revolution scanning device, which is in routine clinical use for cardiac CT scans at the Department of Nuclear Medicine at the University Hospital Zurich. Purpose of this installation is the assessment of the performance of the DLIR algorithm during a limited time span of six weeks.

The algorithm will be CE-marked at the time of installation and use (statement by GE Healthcare provided separately). Its intended use is the reconstruction of CT datasets.

Of note, the novel DLIR algorithm will not substitute any clinical routine procedures currently in use. That is, diagnosis will still be made using the standard reconstruction algorithms.

Primary outcomes

  1. Subjective Image Quality

    Time frame: Day 1

    Subjective image quality as measured by Likert scale from 1 (non-evaluable) to 5 (excellent)

Secondary outcomes

  1. Signal Intensity

    Time frame: Day 1

    Signal intensity as average hounsfield units within a region of interest in the aortic root, change from experimental interventional to the control intervention

  2. Image Noise

    Time frame: Day 1

    Image noise as standard deviation of hounsfield units within a region of interest in the aortic root, change from experimental interventional to the control intervention

  3. Signal-to-noise Ratio

    Time frame: Day 1

    Signal-to-noise ratio

  4. Dose-length Products

    Time frame: Day 1

    Comparison of dose-length products

  5. Plaque Volumes

    Time frame: Day 1

    Quantitative analysis of coronary artery plaque volumes

Sponsors and collaborators

Lead sponsor

University of Zurich

Other

Registry information

Official study title

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

Important dates

Study start
2019
Primary completion
2019
Study completion
2019
First posted
Jun 10, 2019
Registry last updated
Nov 24, 2021

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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