ArticleJournal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance2023
Automated detection of cardiac rest period for trigger delay calculation for image-based navigator coronary magnetic resonance angiography.
Article in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- AI-driven radiomics in vascular disease: a narrative review of image analysis and clinical translation.Cardiovascular diagnosis and therapy · 2026Review
- Artificial intelligence across the cardiovascular diagnostic pathway: a case-based narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
- Improvements in the spatial resolution of coronary magnetic resonance angiography enhance the diagnostic performance in comparison to quantitative coronary angiography.European heart journal. Imaging methods and practice · 2026Article
- Noninvasive coronary imaging: Technological breakthroughs and the integrated application of coronary magnetic resonance angiography.International journal of cardiology. Cardiovascular risk and prevention · 2025Review
- High-resolution automated free-breathing coronary magnetic resonance angiography in comparison with coronary computed tomography angiography.European heart journal. Imaging methods and practice · 2025Article
- The beating heart: artificial intelligence for cardiovascular application in the clinic.Magma (New York, N.Y.) · 2024Review
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Authors and funding
12 authors at 4 institutions in 4 countries.
Funding
Abstract
backgroundCoronary 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.
methodsThirty 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.
resultsThere 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).
conclusionsDeep 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.
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