Study Results
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View full resultsBasic Information
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COMPLETED
NA
50 participants
INTERVENTIONAL
2019-05-08
2019-06-20
Brief Summary
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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.
Detailed Description
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Conditions
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Study Design
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NA
SINGLE_GROUP
DIAGNOSTIC
NONE
Study Groups
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Normal-dose versus Low-dose
The standard intervention consists of the routinely performed cardiac CT datasets reconstructed with a standard iterative reconstruction algorithm (ASIR-V). Median radiation dose is about 0.5 mSv, range between about 0.2 and 1.2 mSv; median contrast agent administration about 45 mL, range between 35 and 55 mL.
The experimental intervention is an additional CT scan with a lower dose (about 20 to 50% decrease) and a similar contrast agent administration that is reconstructed with a deep-learning image reconstruction immediately after the clinical CT scan. The additional time required is about 5 minutes.
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.
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
* Age ≥ 18 years
* Written informed consent
Exclusion Criteria
* Enrollment of the investigator, his/her family members, employees and other dependent persons
* Renal insufficiency (GFR below 35 mL/min/1.73 m²)
18 Years
ALL
No
Sponsors
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University of Zurich
OTHER
Responsible Party
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Ronny R Buechel, MD
PD Dr. med. Ronny R. Buechel
Principal Investigators
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Ronny R Buechel, MD
Role: PRINCIPAL_INVESTIGATOR
Director of Cardiac Imaging
Locations
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University Hospital
Zurich, , Switzerland
Countries
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References
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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.
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.
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.
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.
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.
Provided Documents
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Document Type: Study Protocol and Statistical Analysis Plan
Other Identifiers
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USZ-2019-00533
Identifier Type: -
Identifier Source: org_study_id