ArticleActa radiologica open2022
Neural network-based fully automated cardiac resting phase detection algorithm compared with manual detection in patients.
Article in Acta radiologica open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- 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
- High-resolution automated free-breathing coronary magnetic resonance angiography in comparison with coronary computed tomography angiography.European heart journal. Imaging methods and practice · 2025Article
- Automated detection of cardiac rest period for trigger delay calculation for image-based navigator coronary magnetic resonance angiography.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Article
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Authors and funding
8 authors.
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Abstract
Background: A cardiac resting phase is used when performing free-breathing cardiac magnetic resonance examinations. Purpose: The purpose of this study was to test a cardiac resting phase detection system based on neural networks in clinical practice. Material and Methods: Four chamber-view cine images were obtained from 32 patients and analyzed. The rest duration, start point, and end point were compared between that determined by the experts and general operators, and a similar comparison was done between that determined by the experts and neural networks: the normalized root-mean-square error (RMSE) was also calculated. Results: Unlike manual detection, the neural network was able to determine the resting phase almost simultaneously as the image was obtained. The rest duration and start point were not significantly different between the neural network and expert ( Conclusions: The neural network can determine the resting phase instantly with better accuracy than the manual detection of general operators.
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