Deep-Learning Image Reconstruction in CCTA
NCT03980470 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 50
Last updated 2021-11-24
Summary
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.
Conditions
Interventions
- DEVICE
-
TrueFidelity
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.
Sponsors & Collaborators
-
University of Zurich
lead OTHER
Principal Investigators
-
Ronny R Buechel, MD · Director of Cardiac Imaging
Study Design
- Allocation
- NA
- Purpose
- DIAGNOSTIC
- Masking
- NONE
- Model
- SINGLE_GROUP
Eligibility
- Min Age
- 18 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2019-05-08
- Primary Completion
- 2019-06-20
- Completion
- 2019-06-20
- FDA Device
- Yes
Countries
- Switzerland
Study Locations
More Related Trials
-
International Study of Artificial Intelligence-based Diagnosis of Cardiomyopathy Using Cardiac MRI (AID-MRI)
NCT05793840 ·Status: ENROLLING_BY_INVITATION
-
Artificial Intelligence Delivered Cardiac Magnetic Resonance - Prospective Validation
NCT06061822 ·Status: RECRUITING ·Phase: NA
-
Evaluating New Radiation Techniques for Cardiovascular Imaging
NCT01621594 ·Status: RECRUITING ·Phase: NA
-
AI-enabled Screening and Diagnosis of Cardiomyopathies Using Coronary CTA
NCT06748261 ·Status: NOT_YET_RECRUITING
-
Evaluation of a Free-breathing Cardiac Cine-MRI Sequence With Image Reconstructions by Deep-Learning in Ischemic Heart Disease
NCT05105984 ·Status: COMPLETED
-
Risk Evaluation by COronary Imaging and Artificial intelliGence Based fuNctIonal analyZing tEchniques - III
NCT06793774 ·Status: RECRUITING
-
Improved Prediction of Functional Recovery After Revascularisation Using Combined Assessment of Myocardial Ischaemia and Viability by CMR - Pilot Study
NCT03798652 ·Status: UNKNOWN
-
Cardiac Effects From Radiation Therapy by MRI
NCT04486573 ·Status: COMPLETED ·Phase: NA
-
Application of speCtraL Computed tomogrAphy to impRove specIficity of Cardiac compuTed tomographY
NCT03139006 ·Status: UNKNOWN
-
Evaluating Replacement of Standard-of-care Low Dose Computer Tomography (CT) Scans With Radiation-free Bone Imaging by Deep-learning Augmented Zero Echo Time (DL-ZTE) Magnetic Resonance Tomography (MRT)
NCT06579547 ·Status: RECRUITING ·Phase: NA
-
Role of Cardiac Computed Tomography in Optimising Response to Cardiac Resynchronisation Therapy
NCT02434159 ·Status: UNKNOWN ·Phase: NA
-
Risk Evaluation by COronary CTA and Artificial intelliGence Based fuNctIonal analyZing tEchniques - I
NCT05884008 ·Status: RECRUITING
-
Deep Learning Super-Resolution Single-Beat CMR
NCT07029789 ·Status: COMPLETED
-
Study of Coronary Artery Disease by Two Types of Angiography
NCT00273819 ·Status: COMPLETED ·Phase: NA
-
Deep Learning Reconstruction Algorithms in Dual Low-dose CTA
NCT06372756 ·Status: RECRUITING
-
The Clinical Value of Deep Learning-Based Reconstruction Techniques in Cardiac MRI Scanning
NCT07274436 ·Status: RECRUITING
-
Use of Large Field of View During Image Acquisition for Coronary Angiography
NCT01334931 ·Status: COMPLETED ·Phase: PHASE3
-
New Heart Imaging Techniques to Evaluate Possible Heart Disease
NCT01399385 ·Status: RECRUITING ·Phase: NA
-
Arterial Stiffness for Improved Prediction of Coronary Artery Disease by Coronary CT Angiography
NCT06734247 ·Status: TERMINATED
-
Developing Methods for Reconstructing Electrical Heart Activity
NCT03947021 ·Status: UNKNOWN ·Phase: NA
-
Performance of Acquisition Automation of Cardiac MRI
NCT06012890 ·Status: UNKNOWN
-
Knowledge-based Iterative Model Reconstruction at Low-kilovoltage (kV) Cardiac Computed Tomography (CT)
NCT01896674 ·Status: COMPLETED
-
Clinical Implication CMR in AMI Registry
NCT04788940 ·Status: UNKNOWN
-
Detection and Significance of Heart Injury in ST Elevation Myocardial Infarction.
NCT02072850 ·Status: ACTIVE_NOT_RECRUITING
-
Detecting Heart Disease Using First Pass Imaging With Gated SPECT Perfusion
NCT01137409 ·Status: COMPLETED