Application of speCtraL computed tomogrAphy to impRove specIficity of cardiac compuTed tomographY (CLARITY study): rationale and design

Robbert Willem van Hamersvelt, Ivana Išgum, Pim A de Jong, Maarten Jan Maria Cramer, Geert E H Leenders, Martin J Willemink, Michiel Voskuil, Tim Leiner, Robbert Willem van Hamersvelt, Ivana Išgum, Pim A de Jong, Maarten Jan Maria Cramer, Geert E H Leenders, Martin J Willemink, Michiel Voskuil, Tim Leiner

Abstract

Introduction: Anatomic stenosis evaluation on coronary CT angiography (CCTA) lacks specificity in indicating the functional significance of a stenosis. Recent developments in CT techniques (including dual-layer spectral detector CT [SDCT] and static stress CT perfusion [CTP]) and image analyses (including fractional flow reserve [FFR] derived from CCTA images [FFRCT] and deep learning analysis [DL]) are potential strategies to increase the specificity of CCTA by combining both anatomical and functional information in one investigation. The aim of the current study is to assess the diagnostic performance of (combinations of) SDCT, CTP, FFRCT and DL for the identification of functionally significant coronary artery stenosis.

Methods and analysis: Seventy-five patients aged 18 years and older with stable angina and known coronary artery disease and scheduled to undergo clinically indicated invasive FFR will be enrolled. All subjects will undergo the following SDCT scans: coronary calcium scoring, static stress CTP, rest CCTA and if indicated (history of myocardial infarction) a delayed enhancement acquisition. Invasive FFR of ≤0.80, measured within 30 days after the SDCT scans, will be used as reference to indicate a functionally significant stenosis. The primary study endpoint is the diagnostic performance of SDCT (including CTP) for the identification of functionally significant coronary artery stenosis. Secondary study endpoint is the diagnostic performance of SDCT, CTP, FFRCT and DL separately and combined for the identification of functionally significant coronary artery stenosis.

Ethics and dissemination: Ethical approval was obtained. All subjects will provide written informed consent. Study findings will be disseminated through peer-reviewed conference presentations and journal publications.

Trial registration number: NCT03139006; Pre-results.

Keywords: Computed tomography; cardiovascular imaging; coronary artery disease; fractional flow reserve; machine learning; perfusion.

Conflict of interest statement

Competing interests: II received Research grants from Netherlands Organisation for Scientific Research (NWO)/ Foundation for Technological Sciences (number 12726) with industrial participation (Pie Medical Imaging, 3Mensio Medical Imaging). II and TL received research grants from The Netherlands Organisation for Health Research and Development (FSCAD, number 104003009); Research grants from Netherlands Organisation for Scientific Research (NWO) Domain Applied and Engineering Sciences (AES) (number P15-26) with industrial participation (Pie Medical Imaging, Philips Healthcare); Research grants Pie Medical Imaging. MJW and TL gave lectures for the Philips Healthcare Speakers Bureau.

© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
SDCT scan protocol. MI, myocardial infarction; SDCT, dual-layer spectral detector CT.
Figure 2
Figure 2
Flow chart of degree of stenosis analysis combined with FFRCT, CTP and DL (including dual-energy CT options provided by SDCT). First, degree of stenosis will be evaluated. If ≥25% degree of stenosis is present, further testing using either FFRCT, CTP or DL will be used to indicate a functionally significant stenosis. CCTA, coronary CT angiography; CTP, CT perfusion; DL, deep learning; FFRCT, fractional flow reserve derived from coronary CT angiography images; SDCT, dual-layer spectral detector CT.

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