Super‑resolution deep learning image reconstruction: image quality and myocardial homogeneity in coronary computed tomography angiography

키워드

참고문헌 (35)

1. Hamilton-Craig CR, "Cardiac computed tomography : evidence, limitations and clinical application", Heart Lung Circ, 21, pp.70~81 (2012)
2. SCOT-HEART Investigators, "Coronary CT angiography and 5-year risk of myocardial infarction", N Engl J Med, 379, pp.924~933 (2018)
3. Knuuti J, "The performance of non-invasive tests to rule-in and rule-out significant coronary artery stenosis in patients with stable angina : a meta-analysis focused on post-test disease probability", Eur Heart J, 39, pp.3322~3330 (2018)
4. Heseltine TD, "Latest advances in cardiac CT", Eur Cardiol, 15, pp.1~7 (2020)
5. Budoff MJ, "Cardiac CT angiography in current practice : an American society for preventive cardiology clinical practice statement✰", Am J Prev Cardiol, 9, pp.100318~ (2022)
6. Kang EJ, "Clinical applications of wide-detector CT scanners for cardiothoracic imaging : an update", Korean J Radiol, 20, pp.1583~1596 (2019)
7. Toia P, "Technical development in cardiac CT : current standards and future improvements : a narrative review", Cardiovasc Diagn Ther, 10, pp.2018~2035 (2020)
8. Wang M, "A deep-learning reconstruction algorithm that improves the image quality of low-tube-voltage coronary CT angiography", Eur J Radiol, 146, pp.110070~ (2022)
9. Otgonbaatar C, "Improvement in image quality and visibility of coronary arteries, stents, and valve structures on CT angiography by deep learning reconstruction", Korean J Radiol, 23, pp.1044~1054 (2022)
10. Wykrzykowska JJ, "Assessment of in-stent restenosis using 64-MDCT : analysis of the CORE-64 Multicenter International Trial", AJR Am J Roentgenol, 194, pp.85~92 (2010)
11. Arbab-Zadeh A, "Diagnostic accuracy of computed tomography coronary angiography according to pre-test probability of coronary artery disease and severity of coronary arterial calcification. The CORE-64(Coronary Artery Evaluation Using 64-Row Multidetector Computed Tomography Angiography)International Multicenter Study", J Am Coll Cardiol, 59, pp.379~387 (2012)
12. Oostveen LJ, "Physical evaluation of an ultra-high-resolution CT scanner", Eur Radiol, 30, pp.2552~2560 (2020)
13. Nagayama Y, "Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography", Eur Radiol, 33, pp.8488~8500 (2023)
14. Takagi H, "Diagnostic performance of coronary CT angiography with ultra-high-resolution CT : comparison with invasive coronary angiography", Eur J Radiol, 101, pp.30~37 (2018)
15. Lee TC, "Deep learning enabled wide-coverage highresolution cardiac", Medical imaging 2022: physics of medical imaging. SPIE medical imaging, 12031 (2022), SPIE
16. Greffier J, "Comparison of two deep-learning image reconstruction algorithms on cardiac CT images : a phantom study", Diagn Interv Imaging, 105, pp.110~117 (2024)
17. Nagayama Y, "Coronary stent evaluation by CTA : image quality comparison between super-resolution deep learning reconstruction and other reconstruction algorithms", AJR Am J Roentgenol, 221, pp.599~610 (2023)
18. Takafuji M, "Super-resolution deep learning reconstruction for improved image quality of coronary CT angiography", Radiol Cardiothorac Imaging, 5, pp.e230085~ (2023)
19. Nikolaou K, "Assessment of myocardial perfusion and viability from routine contrastenhanced 16-detector-row computed tomography of the heart : preliminary results", Eur Radiol, 15, pp.864~871 (2005)
20. Varga-Szemes A, "CT myocardial perfusion imaging", AJR Am J Roentgenol, 204, pp.487~497 (2015)
21. Rossi A, "Stress myocardial perfusion : imaging with multidetector CT", Radiology, 270, pp.25~46 (2014)
22. Clayton B, "Assessment of the myocardium with cardiac computed tomography", Eur Heart J Cardiovasc Imaging, 15, pp.603~609 (2014)
23. Larke FJ, "Estimated radiation dose associated with low-dose chest CT of average-size participants in the National Lung Screening Trial", AJR Am J Roentgenol, 197, pp.1165~1169 (2011)
24. Koo TK, "A guideline of selecting and reporting intraclass correlation coefficients for reliability research", J Chiropr Med, 15, pp.155~163 (2016)
25. Tatsugami F, "Deep learningbased image restoration algorithm for coronary CT angiography", Eur Radiol, 29, pp.5322~5329 (2019)
26. Benz DC, "Validation of deep-learning image reconstruction for coronary computed tomography angiography : impact on noise, image quality and diagnostic accuracy", J Cardiovasc Comput Tomogr, 14, pp.444~451 (2020)
27. Racine D, "Taskbased characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT : a phantom study", Phys Med, 76, pp.28~37 (2020)
28. Nakamura Y, "Possibility of deep learning in medical imaging focusing improvement of computed tomography image quality", J Comput Assist Tomogr, 44, pp.161~167 (2020)
29. Tatsugami F, "Improvement of spatial resolution on coronary CT angiography by using super-resolution deep learning reconstruction", Acad Radiol, 30, pp.2497~2504 (2023)
30. Neumann FJ, "2018 ESC", EACTS guidelines on myocardial revascularization, 2019, pp.40, 87~165
31. Cassese S, "Incidence and predictors of restenosis after coronary stenting in 10 004 patients with surveillance angiography", Heart, 100, pp.153~159 (2014)
32. Godino C, "Clinical and angiographic follow-up of small vessel lesions treated with paclitaxel-eluting stents(from the TRUE Registry)", Am J Cardiol, 102, pp.1002~1008 (2008)
33. Silverio A, "Percutaneous treatment and outcomes of small coronary vessels : a SCAAR report", JACC Cardiovasc Interv, 13, pp.793~804 (2020)
34. Saito S, "Two-year results after coronary stenting of small vessels in Japanese population using 2. 25-mm diameter sirolimus-eluting stent with bioresorbable polymer : primary and long-term outcomes of CENTURY JSV study", Cardiovasc Interv Ther, 34, pp.25~33 (2019)
35. Stone GW, "A prospective natural-history study of coronary atherosclerosis", N Engl J Med, 364, pp.226~235 (2011)

관련 논문AI추천