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

BMJ Open. 2019 Mar 1;9(3):e025793. doi: 10.1136/bmjopen-2018-025793.


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.

Publication types

  • Clinical Trial Protocol
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Cardiac Imaging Techniques / methods
  • Computed Tomography Angiography / methods*
  • Controlled Clinical Trials as Topic / methods*
  • Coronary Angiography / methods*
  • Coronary Stenosis / diagnostic imaging*
  • Fractional Flow Reserve, Myocardial
  • Humans
  • Machine Learning
  • Middle Aged
  • Multimodal Imaging / methods
  • Prospective Studies
  • Sample Size
  • Young Adult

Associated data