Automated detection of cardiac rest period for trigger delay calculation for image-based navigator coronary magnetic resonance angiography

Gregory Wood, Alexandra Uglebjerg Pedersen, Karl P Kunze, Radhouene Neji, Reza Hajhosseiny, Jens Wetzl, Seung Su Yoon, Michaela Schmidt, Bjarne Linde Nørgaard, Claudia Prieto, René M Botnar, Won Yong Kim

Research output: Contribution to journalArticlepeer-review


Background: Coronary magnetic resonance angiography (coronary MRA) is increasingly being considered as a clinically viable method to investigate coronary artery disease (CAD). Accurate determination of the trigger delay to place the acquisition window within the quiescent part of the cardiac cycle is critical for coronary MRA in order to reduce cardiac motion. This is currently reliant on operator-led decision making, which can negatively affect consistency of scan acquisition. Recently developed deep learning (DL) derived software may overcome these issues by automation of cardiac rest period detection. Methods: Thirty individuals (female, n = 10) were investigated using a 0.9 mm isotropic image-navigator (iNAV)-based motion-corrected coronary MRA sequence. Each individual was scanned three times utilising different strategies for determination of the optimal trigger delay: (1) the DL software, (2) an experienced operator decision, and (3) a previously utilised formula for determining the trigger delay. Methodologies were compared using custom-made analysis software to assess visible coronary vessel length and coronary vessel sharpness for the entire vessel length and the first 4 cm of each vessel. Results: There was no difference in image quality between any of the methodologies for determination of the optimal trigger delay, as assessed by visible coronary vessel length, coronary vessel sharpness for each entire vessel and vessel sharpness for the first 4 cm of the left mainstem, left anterior descending or right coronary arteries. However, vessel length of the left circumflex was slightly greater using the formula method. The time taken to calculate the trigger delay was significantly lower for the DL-method as compared to the operator-led approach (106 ± 38.0 s vs 168 ± 39.2 s, p < 0.01, 95% CI of difference 25.5–98.1 s). Conclusions: Deep learning-derived automated software can effectively and efficiently determine the optimal trigger delay for acquisition of coronary MRA and thus may simplify workflow and improve reproducibility.

Original languageEnglish
Article number52
Pages (from-to)52
JournalJournal of Cardiovascular Magnetic Resonance
Issue number1
Publication statusPublished - 2 Oct 2023


  • Humans
  • Female
  • Magnetic Resonance Angiography/methods
  • Reproducibility of Results
  • Predictive Value of Tests
  • Heart
  • Coronary Vessels/diagnostic imaging
  • Coronary Angiography/methods
  • Imaging, Three-Dimensional

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