Abstract: Purpose: To develop and validate a deep learning (DL)-model for automatic reconstruction for coronary CT angiography (CCTA) in patients with origin anomaly, stent or bypass graft.
Material and Methods: In this retrospective study, a DL model for automatic CCTA reconstruction was developed with training and validation sets from 6063 and 1962 patients. The algorithm was evaluated on an independent external test set of 812 patients (357 with origin anomaly or revascularization, 455 without). The image quality of DL reconstruction and manual reconstruction (using dedicated cardiac reconstruction software provided by CT vendors) was compared using a 5-point scale. The successful reconstruction rates and post-processing time for two methods were recorded.
Results: In the external test set, 812 patients (mean age, 64.0 ± 11.6, 100 with origin anomalies, 152 with stents, 105 with bypass grafts) were evaluated. The successful rates for automatic reconstruction were 100% (455/455), 97% (97/100), 100% (152/152), and 76.2% (80/105) in patients with native vessel, origin anomaly, stent, and bypass graft, respectively. The image quality scores were significantly higher for DL reconstruction than those for manual approach in all subgroups (4 vs. 3 for native vessel, 4 vs. 4 for origin anomaly, 4 vs. 3 for stent and 4 vs. 3 for bypass graft, all p < 0.001). The overall post-processing time was remarkably reduced for DL reconstruction compared to manual method (11 s vs. 465 s, p < 0.001).
Conclusions: The developed DL model enabled accurate automatic CCTA reconstruction of bypass graft, stent and origin anomaly. It significantly reduced post-processing time and improved clinical workflow.
(© 2024. Italian Society of Medical Radiology.)
References: Haase R, Schlattmann P, Gueret P et al (2019) Diagnosis of obstructive coronary artery disease using computed tomography angiography in patients with stable chest pain depending on clinical probability and in clinically important subgroups: meta-analysis of individual patient data. BMJ 365:l1945. https://doi.org/10.1136/bmj.l1945. (PMID: 10.1136/bmj.l1945311896176561308)
Gulati M, Levy PD, Mukherjee D et al (2021) 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR guideline for the evaluation and diagnosis of chest pain: a report of the American College of Cardiology/American Heart Association Joint Committee on clinical practice guidelines. Circulation 144:e368–e454. https://doi.org/10.1161/cir.0000000000001029. (PMID: 10.1161/cir.000000000000102934709928)
Knuuti J, Wijns W, Saraste A et al (2020) 2019 ESC guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J 41:407–477. https://doi.org/10.1093/eurheartj/ehz425. (PMID: 10.1093/eurheartj/ehz42531504439)
Reeves RA, Halpern EJ, Rao VM (2021) Cardiac imaging trends from 2010 to 2019 in the medicare population. Radiol Cardiothorac Imaging 3:e210156. https://doi.org/10.1148/ryct.2021210156. (PMID: 10.1148/ryct.2021210156347787858581585)
Einstein AJ, Hirschfeld C, Williams MC et al (2022) Worldwide disparities in recovery of cardiac testing 1 year into COVID-19. J Am Coll Cardiol 79:2001–2017. https://doi.org/10.1016/j.jacc.2022.03.348. (PMID: 10.1016/j.jacc.2022.03.348355891629109706)
Liu K, Hsieh C, Zhuang N et al (2016) Current utilization of cardiac computed tomography in mainland China: a national survey. J Cardiovasc Comput Tomogr 10:76–81. https://doi.org/10.1016/j.jcct.2015.11.004. (PMID: 10.1016/j.jcct.2015.11.00426699712)
Hilkewich MW (2014) Written observations as a part of computed tomography angiography post processing by medical radiation technologists: a pilot project. J Med Imaging Radiat Sci 45:31-36.e31. https://doi.org/10.1016/j.jmir.2013.10.012. (PMID: 10.1016/j.jmir.2013.10.01231051994)
Castiglioni I, Rundo L, Codari M et al (2021) AI applications to medical images: from machine learning to deep learning. Phys Med 83:9–24. https://doi.org/10.1016/j.ejmp.2021.02.006. (PMID: 10.1016/j.ejmp.2021.02.00633662856)
Lesage D, Angelini ED, Bloch I, Funka-Lea G (2009) A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med Image Anal 13:819–845. https://doi.org/10.1016/j.media.2009.07.011. (PMID: 10.1016/j.media.2009.07.01119818675)
Li Y, Wu Y, He J et al (2022) Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography. Eur Radiol 32:6037–6045. https://doi.org/10.1007/s00330-022-08761-z. (PMID: 10.1007/s00330-022-08761-z35394183)
Xu L, He Y, Luo N et al (2021) Diagnostic accuracy and generalizability of a deep learning-based fully automated algorithm for coronary artery stenosis detection on CCTA: a multi-centre registry study. Front Cardiovasc Med 8:707508. https://doi.org/10.3389/fcvm.2021.707508. (PMID: 10.3389/fcvm.2021.707508348052978602896)
Han D, Liu J, Sun Z, Cui Y, He Y, Yang Z (2020) Deep learning analysis in coronary computed tomographic angiography imaging for the assessment of patients with coronary artery stenosis. Comput Methods Programs Biomed 196:105651. https://doi.org/10.1016/j.cmpb.2020.105651. (PMID: 10.1016/j.cmpb.2020.10565132712571)
Zhang X, Zhang J, Ma L et al (2022) Progressive deep segmentation of coronary artery via hierarchical topology learning. In: International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 391–400.
Dai X, Yu M, Pan J et al (2019) Image quality and diagnostic accuracy of coronary CT angiography derived from low-dose dynamic CT myocardial perfusion: a feasibility study with comparison to invasive coronary angiography. Eur Radiol 29:4349–4356. https://doi.org/10.1007/s00330-018-5777-4. (PMID: 10.1007/s00330-018-5777-430413956)
Fu F, Wei J, Zhang M et al (2020) Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network. Nat Commun 11:4829. https://doi.org/10.1038/s41467-020-18606-2. (PMID: 10.1038/s41467-020-18606-2329731547518426)
Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18:203–211. https://doi.org/10.1038/s41592-020-01008-z. (PMID: 10.1038/s41592-020-01008-z33288961)
Jiang B, Li N, Shi X et al (2022) Deep learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT. Radiology 303(1):202–212. https://doi.org/10.1148/radiol.210551. (PMID: 10.1148/radiol.21055135040674)
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