Poon M et al (2020) Current evidence and recommendations for coronary CTA first in evaluation of stable coronary artery disease. J Am Coll Cardiol 76(11):1358–1362
Kawashima H et al (2022) Diagnostic concordance and discordance between angiography-based quantitative flow ratio and fractional flow reserve derived from computed tomography in complex coronary artery disease. J Cardiovasc Comput Tomogr 16(4):336–342
Rgensen JO et al (2017) Functional testing or coronary computed tomography angiography in patients with stable coronary artery disease. J Am Coll Cardiol 69(14):1761–1770
Hajhosseiny R et al (2020) Coronary magnetic resonance angiography: technical innovations leading us to the promised land? JACC Cardiovasc Imaging 13(12):2653–2672
Guo R et al (2022) Emerging techniques in cardiac magnetic resonance imaging. J Magn Reson Imaging 55(4):1043–1059
Ota H et al (2024) Motion robust coronary MR angiography using zigzag centric ky–kz trajectory and high-resolution deep learning reconstruction. Magn Reson Mater Phys Biol Med 37(6):1105–1117
Yokota Y et al (2021) Effects of deep learning reconstruction technique in high-resolution non-contrast magnetic resonance coronary angiography at a 3-tesla machine. Can Assoc Radiol J 72(1):120–127
Chung H et al (2020) Stenosis detection from time-of-flight magnetic resonance angiography via deep learning 3d squeeze and excitation residual networks. IEEE Access 8:43325–43335
Liu L et al (2017) Fully automated segmentation of coronary lumen based on the directional minimal path and image fusion. In: 2017 6th international conference on computer science and network technology (ICCSNT)
Wang Y, Meng X and Wang D (2010) Automated coronary artery analysis system in 3D CTA images. In: 2010 3rd international conference on biomedical engineering and informatics.
Chen ST, Huang CY, Chen CM (2012) Automatic segmentation of coronary arteries based on region growing and discrete wavelet transformation. In: 2012 international conference on computing, measurement, control and sensor network.
Gharleghi R et al (2022) Towards automated coronary artery segmentation: a systematic review. Comput Methods Programs Biomed 225:107015
Lian J et al (2023) Machine learning in medicine—focus on radiology. Machine learning, medical AI and robotics: translating theory into the clinic. IOP Publishing Bristol, UK, pp 1–1
Isensee F et al (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18(2):203–211
Article CAS PubMed Google Scholar
Zhang G et al (2021) Multiorgan segmentation from partially labeled datasets with conditional nnU-Net. Comput Biol Med 136:104658
Schuetz GM et al (2010) Meta-analysis: noninvasive coronary angiography using computed tomography versus magnetic resonance imaging. Ann Intern Med 152(3):167–177
Fonseca B, Da Cruz EM, Jaggers J (2020) Anomalies of the coronary arteries. Critical care of children with heart disease: basic medical and surgical concepts. Springer, Cham, pp 419–432
Androulakis E, Mohiaddin R, Bratis K (2022) Magnetic resonance coronary angiography in the era of multimodality imaging. Clin Radiol 77(7):e489–e499
Article CAS PubMed Google Scholar
Gharleghi R et al (2022) Automated segmentation of normal and diseased coronary arteries—the ASOCA challenge. Comput Med Imaging Graph 97:102049
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. Springer, Cham p, pp 234–241
Milletari F, Navab N, Ahmadi S (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. IEEE, New York, pp 565–571
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. IEEE, New York, pp 3431–3440
Sun B et al (2020) A direct comparison of 3 T contrast-enhanced whole-heart coronary cardiovascular magnetic resonance angiography to dual-source computed tomography angiography for detection of coronary artery stenosis: a single-center experience. J Cardiovasc Magn Reson 22(1):40
Article PubMed PubMed Central Google Scholar
Di Leo G et al (2016) Diagnostic accuracy of magnetic resonance angiography for detection of coronary artery disease: a systematic review and meta-analysis. Eur Radiol 26(10):3706–3718
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