Deep-Learning Image Reconstruction in CCTA

Coronary Artery Disease Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03980470
• Other study ID #: USZ-2019-00533
• Status: Completed
• Phase: N/A
• Start date: May 8, 2019
• Est. completion date: June 20, 2019

Study information

• Verified date: October 2021
• Source: University of Zurich
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 50
• Est. completion date: June 20, 2019
• Est. primary completion date: June 20, 2019
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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)

Related Conditions & MeSH terms

• Coronary Artery Disease

Intervention

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.

Locations

Country	Name	City	State
Switzerland	University Hospital	Zurich

Sponsors (1)

Lead Sponsor	Collaborator
University of Zurich

Country where clinical trial is conducted

Switzerland, 

References & Publications (5)

Benz DC, Fuchs TA, Gräni C, Studer Bruengger AA, Clerc OF, Mikulicic F, Messerli M, Stehli J, Possner M, Pazhenkottil AP, Gaemperli O, Kaufmann PA, Buechel RR. Head-to-head comparison of adaptive statistical and model-based iterative reconstruction algorithms for submillisievert coronary CT angiography. Eur Heart J Cardiovasc Imaging. 2018 Feb 1;19(2):193-198. doi: 10.1093/ehjci/jex008. — View Citation

Benz DC, Gräni C, Hirt Moch B, Mikulicic F, Vontobel J, Fuchs TA, Stehli J, Clerc OF, Possner M, Pazhenkottil AP, Gaemperli O, Buechel RR, Kaufmann PA. A low-dose and an ultra-low-dose contrast agent protocol for coronary CT angiography in a clinical setting: quantitative and qualitative comparison to a standard dose protocol. Br J Radiol. 2017 Jun;90(1074):20160933. doi: 10.1259/bjr.20160933. Epub 2017 May 25. — View Citation

Benz DC, Gräni C, Hirt Moch B, Mikulicic F, Vontobel J, Fuchs TA, Stehli J, Clerc OF, Possner M, Pazhenkottil AP, Gaemperli O, Buechel RR, Kaufmann PA. Minimized Radiation and Contrast Agent Exposure for Coronary Computed Tomography Angiography: First Clinical Experience on a Latest Generation 256-slice Scanner. Acad Radiol. 2016 Aug;23(8):1008-14. doi: 10.1016/j.acra.2016.03.015. Epub 2016 May 9. — View Citation

Sahiner B, Pezeshk A, Hadjiiski LM, Wang X, Drukker K, Cha KH, Summers RM, Giger ML. Deep learning in medical imaging and radiation therapy. Med Phys. 2019 Jan;46(1):e1-e36. doi: 10.1002/mp.13264. Epub 2018 Nov 20. Review. — View Citation

Toprak O. Conflicting and new risk factors for contrast induced nephropathy. J Urol. 2007 Dec;178(6):2277-83. Epub 2007 Oct 22. Review. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Subjective Image Quality	Subjective image quality as measured by Likert scale from 1 (non-evaluable) to 5 (excellent)	Day 1
Secondary	Signal Intensity	Signal intensity as average hounsfield units within a region of interest in the aortic root, change from experimental interventional to the control intervention	Day 1
Secondary	Image Noise	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	Day 1
Secondary	Signal-to-noise Ratio	Signal-to-noise ratio	Day 1
Secondary	Dose-length Products	Comparison of dose-length products	Day 1
Secondary	Plaque Volumes	Quantitative analysis of coronary artery plaque volumes	Day 1