Clinical trial NCT03980470: Deep-Learning Image Reconstruction in CCTA
Official title: Usefulness of Deep-Learning Image Reconstruction for Cardiac Computed Tomography Angiography - a Prospective, Non-randomized Observational Trial
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Coronary Artery Disease
Lead sponsor: University of Zurich
Sponsor class: OTHER
First posted: 2019-06-10
Enrollment: 50
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

Brief 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.


Eligibility criteria: 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)

Primary outcomes:
measure: Subjective Image Quality
timeFrame: Day 1
Secondary outcomes:
measure: Signal Intensity
timeFrame: Day 1
measure: Image Noise
timeFrame: Day 1
measure: Signal-to-noise Ratio
timeFrame: Day 1
measure: Dose-length Products
timeFrame: Day 1
measure: Plaque Volumes
timeFrame: Day 1
Other outcomes:
Locations:
city: Zurich
country: Switzerland
facility: University Hospital
Study officials:
affiliation: Director of Cardiac Imaging
name: Ronny R Buechel, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Benz DC, Grani 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.
citation: Benz DC, Fuchs TA, Grani 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.
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.
citation: Toprak O. Conflicting and new risk factors for contrast induced nephropathy. J Urol. 2007 Dec;178(6):2277-83. doi: 10.1016/j.juro.2007.08.054. Epub 2007 Oct 22.
citation: Benz DC, Grani 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.