Authors : Sudha Surikuchi

Volume/Issue : Volume 11 - 2026, Issue 1 - January

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Google Scholar : https://tinyurl.com/bddctpec

Scribd : https://tinyurl.com/mtuwr5kz

DOI : https://doi.org/10.38124/ijisrt/26jan1317

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : Coronary Artery Disease (CAD) remains one of the leading causes of death globally. Early detection and risk stratification are very crucial for effective management as well as prognosis. Radiological imaging modalities such as the Coronary Computed Tomography Angiography (CCTA), Cardiac Magnetic Resonance Imaging (CMR, and Nuclear Imaging, play a pivotal role in non-invasive evaluation. This paper explores the integrative use of those radiological techniques to enhance diagnostic accuracy, guide clinical choice-making, and personalize treatment plans. The observer synthesizes current improvements, evaluates comparative effectiveness, and highlights future directions.

Keywords : Coronary Artery Disease, CCTA, CMR, SPECT, Risk Stratification, Radiological Imaging.

References :

1. Al'Aref, S. J., Min, J. K., & Dey, D. (2019). Machine learning and artificial intelligence in coronary CT angiography: Current status and future directions. Radiology: Cardiothoracic Imaging, 1(5), e190064. https://doi.org/10.1148/ryct.2019190064
2. Antonopoulos, A. S., Sanna, F., Sabharwal, N., et al. (2017). Detecting human coronary inflammation by imaging perivascular fat. European Heart Journal, 38(46), 3569–3578. https://doi.org/10.1093/eurheartj/ehx509
3. Arsanjani, R., Dey, D., Khachatryan, T., et al. (2019). Prediction of revascularization after myocardial perfusion SPECT by machine learning in a multi-center study. Journal of Nuclear Cardiology, 26(3), 878–887. https://doi.org/10.1007/s12350-017-1119-9
4. Assomull, R. G., et al. (2014). Prognostic value of combined magnetic resonance myocardial perfusion imaging and late gadolinium enhancement in patients with suspected CAD. International Journal of Cardiovascular Imaging, 30(5), 705–714. https://doi.org/10.1007/s10554-011-9863-9
5. Betancur, J., Commandeur, F., Motlagh, M., et al. (2018). Deep learning for prediction of obstructive disease from myocardial perfusion SPECT: A multicenter study. Journal of Nuclear Medicine, 59(10), 1640–1646. https://doi.org/10.2967/jnumed.118.210435
6. Blankstein, R., & Schlett, C. L. (2021). Multimodality imaging of coronary artery disease. Circulation, 143(6), 609–626. https://doi.org/10.1161/CIRCULATIONAHA.120.046450
7. Danad, I., Szymonifka, J., Twisk, J. W., et al. (2014). Diagnostic performance of cardiac computed tomography angiography for coronary artery disease: A meta-analysis. Journal of the American College of Cardiology, 64(12), 1205–1215. https://doi.org/10.1016/j.jacc.2014.06.1196
8. Finke, D., et al. (2021). Non-invasive multimodal imaging for diagnosis and management of coronary artery disease: A systematic review. Heart, 107(6), 485–492. https://doi.org/10.1136/heartjnl-2020-317456
9. Gul, K., et al. (2013). Late gadolinium enhancement cardiovascular magnetic resonance is complementary to ejection fraction in predicting prognosis of patients with stable coronary artery disease. Journal of Cardiovascular Magnetic Resonance, 15, 84. https://doi.org/10.1186/1532-429X-15-84
10. Kramer, C. M., Barkhausen, J., Flamm, S. D., Kim, R. J., & Nagel, E. (2020). Standardized cardiovascular magnetic resonance imaging (CMR) protocols: 2020 update. Journal of Cardiovascular Magnetic Resonance, 22(1), 17. https://doi.org/10.1186/s12968-020-00607-1
11. Motwani, M., Dey, D., Berman, D. S., et al. (2017). Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: A registry-based study. The Lancet, 389(10083), 899–908. https://doi.org/10.1016/S0140-6736(16)32340-X
12. Motoyama, S., Ito, H., Sarai, M., et al. (2015). Plaque characterization by coronary computed tomography angiography and likelihood of acute coronary events in mid-term follow-up. Journal of the American College of Cardiology, 66(4), 337–346. https://doi.org/10.1016/j.jacc.2015.05.069
13. Neilan, T. G., Coelho-Filho, O. R., Danik, S. B., et al. (2011). CMR quantification of myocardial scar provides additive prognostic information in patients with coronary artery disease. Journal of the American College of Cardiology, 57(18), 1758–1767. https://doi.org/10.1016/j.jacc.2010.11.045
14. Nudi, F., et al. (2013). Diagnostic accuracy of cardiac PET versus SPECT for coronary artery disease: A meta-analysis. Circulation: Cardiovascular Imaging, 6(5), 715–724. https://doi.org/10.1161/CIRCIMAGING.112.978270
15. Oikonomou, E. K., et al. (2018). Non-invasive detection of coronary inflammation using computed tomography and radiomic analysis of perivascular adipose tissue. European Heart Journal, 39(28), 2564–2575. https://doi.org/10.1093/eurheartj/ehy202
16. Oikonomou, E. K., et al. (2023). Coronary computed tomography angiography-derived plaque characteristics and cardiovascular outcomes: A state-of-the-art review. Journal of Cardiovascular Computed Tomography, 17(3), 198–210. https://doi.org/10.1016/j.jcct.2023.03.006
17. Patel, M. R., Dai, D., Hernandez, A. F., et al. (2010). Prevalence and predictors of nonobstructive coronary artery disease identified with coronary angiography in contemporary clinical practice: Findings from the NCDR CathPCI registry. Circulation, 122(13), 1354–1363. https://doi.org/10.1161/CIRCULATIONAHA.109.924977
18. Roth, G. A., Mensah, G. A., Johnson, C. O., et al. (2020). Global burden of cardiovascular diseases, 1990–2019. Journal of the American College of Cardiology, 76(25), 2982–3021. https://doi.org/10.1016/j.jacc.2020.11.010
19. Taqueti, V. R., et al. (2023). Prognostic value of myocardial flow reserve by PET imaging: Systematic review and meta-analysis. Journal of Nuclear Cardiology. https://doi.org/10.1007/s12350-023-03158-0g
20. Tesche, C., Otaki, Y., Gransar, H., et al. (2022). Deep learning analysis of coronary CT angiography for prediction of future major adverse cardiovascular events: Results from the CONFIRM registry. European Heart Journal, 43(10), 1038–1047. https://doi.org/10.1093/eurheartj/ehab892
21. Virani, S. S., Alonso, A., Aparicio, H. J., et al. (2021). Heart disease and stroke statistics—2021 update: A report from the American Heart Association. Circulation, 143(8), e254–e743. https://doi.org/10.1161/CIR.0000000000000950
22. Whittemore, R., & Knafl, K. (2005). The integrative review: Updated methodology. Journal of Advanced Nursing, 52(5), 546–553. https://doi.org/10.1111/j.1365-2648.2005.03621.x
23. Zheng, K., et al. (2023). Prognostic value of positron emission tomography-derived myocardial flow reserve: A systematic review and meta-analysis. Journal of Nuclear Medicine, 64(10), 1572–1581. https://doi.org/10.2967/jnumed.122.264650
24. Zhou, Y., et al. (2023). Artificial intelligence in coronary computed tomography angiography: Clinical demands and solutions. European Radiology, 33(6), 4067–4078. https://doi.org/10.1007/s00330-022-09292-1
25. Libby, P., Buring, J. E., Badimon, L., Hansson, G. K., Deanfield, J., Bittencourt, M. S., Tokgözoğlu, L., & Lewis, E. F. (2019). Atherosclerosis. Nature Reviews Disease Primers, 5(1), 56. https://doi.org/10.1038/s41572-019-0106-z
26. Pontone, G., Baggiano, A., Andreini, D., Guaricci, A. I., & Muscogiuri, G. (2021). Multimodality imaging in coronary artery disease: An integrated approach. JACC: Cardiovascular Imaging, 14(5), 1071–1087. https://doi.org/10.1016/j.jcmg.2020.08.021
27. Henein M.Y., et al. Coronary Atherosclerosis Imaging. Diagnostics (MDPI). 2020;10(2):65.
    DOI: 10.3390/diagnostics10020065
28. Kolossváry M., Szilveszter B., Merkely B., Maurovich-Horvat P. Plaque imaging with CT — a comprehensive review on coronary CT angiography based risk assessment. Cardiovascular Diagnosis and Therapy. 2017 Oct;7(5):489–506.
    DOI: 10.21037/cdt.2016.11.06
29. Rischpler C., et al. Cardiac PET/MRI—an update. EJNMMI Radiopharmacy and Chemistry / EJNMMI Reports (review). 2019.
    DOI: 10.1186/s41824-018-0050-2
30. Chan, R. H., Maron, B. J., Olivotto, I., Assenza, G. E., Haas, T., Lesser, J. R., Gruner, C., Crean, A. M., Rowin, E. J., Garberich, R. F., Manning, W. J., Appelbaum, E., Gibson, C. M., Udelson, J. E., Seidman, C. E., Seidman, J. G., Ho, C. Y., & Maron, M. S. (2024). Late gadolinium enhancement predicts sudden death risk over long-term follow-up in patients with hypertrophic cardiomyopathy. Journal of Cardiovascular Magnetic Resonance, 26(1), Article 103230. https://doi.org/10.1016/j.jcmr.2023.103230
31. Halliday, B. P., Cleland, J. G. F., Goldberger, J. J., Prasad, S. K., & the Clinical Radiology Review Team. (2023). The extent of late gadolinium enhancement predicts mortality, sudden death and major adverse cardiovascular events in patients with nonischaemic cardiomyopathy: A systematic review and meta-analysis. Clinical Radiology, 78(4), e342–e349. https://doi.org/10.1016/j.crad.2022.12.015
32. Giubbini, R., & Milan, E. (2024). Current role of myocardial blood flow quantification with PET/CT in the management of coronary artery disease. International Journal of Cardiovascular Sciences, 37, e20240115. https://doi.org/10.36660/ijcs.20240115
33. Woodhead, R. C., Tong, X., Khan, S. S., Shah, N. S., Jackson, S. L., Loustalot, F., & Vaughan, A. S. (2024). Hybrid PET/CT and PET/MR in Coronary Artery Disease: An Update for Clinicians, with Insights into AI-Guided Integration. J. Cardiovascular Development and Disease, 12(9), 338. https://doi.org/10.3390/jcdd12090338