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Компьютерная томография в кардиологии: история и перспективы
Компьютерная томография в кардиологии: история и перспективы
Миронова О.Ю., Исаев Г.О., Бердышева М.В., Фомин В.В. Компьютерная томография в кардиологии: история и перспективы. Терапевтический архив. 2023;95(9):818–821.
DOI: 10.26442/00403660.2023.09.202377
© ООО «КОНСИЛИУМ МЕДИКУМ», 2023 г.
DOI: 10.26442/00403660.2023.09.202377
© ООО «КОНСИЛИУМ МЕДИКУМ», 2023 г.
________________________________________________
Материалы доступны только для специалистов сферы здравоохранения. Авторизуйтесь или зарегистрируйтесь.
Аннотация
В обзорной статье освещены основные этапы становления компьютерной томографии (КТ) как ключевого метода, использующегося в современной кардиологии. Прогресс КТ-томографов напрямую связан с увеличением количества детекторов, т.е. с увеличением числа одновременно собираемых проекций. Обсуждаются современные разработки и перспективные технологии в области дальнейшего развития методики, включая КТ-ангиографию и другие новые методы оценки коронарного кровотока. Применение технологий искусственного интеллекта в дальнейшем, возможно, позволит усовершенствовать и ускорить интерпретацию получаемых изображений, а также будет экономически оправданно.
Ключевые слова: компьютерная томография (КТ), КТ-ангиография, кардиология, коронарный кровоток, кровообращение, искусственный интеллект
Keywords: computed tomography (CT), coronary CT angiography, cardiology, coronary circulation, artificial intelligence
Ключевые слова: компьютерная томография (КТ), КТ-ангиография, кардиология, коронарный кровоток, кровообращение, искусственный интеллект
________________________________________________
Keywords: computed tomography (CT), coronary CT angiography, cardiology, coronary circulation, artificial intelligence
Полный текст
Список литературы
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14. Knuuti J, Wijns W, Saraste A, et al. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes: The Task Force for the diagnosis and management of chronic coronary syndromes of the European Society of Cardiology (ESC). Eur Heart J. 2020;41(3):407-77. DOI:10.1093/eurheartj/ehz425
15. Gulati M, Levy P, Mukherjee D, et al. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain. J Am Coll Cardiol.
2021;78(22):e187-285. DOI:10.1016/j.jacc.2021.07.053
16. De Bruyne B, Sarma J. Fractional flow reserve: a review. Heart. 2008;94(7):949-59. DOI:10.1136/hrt.2007.122838
17. Brueren BRG, Ten Berg JM, Suttorp MJ, et al. How good are experienced cardiologists at predicting the hemodynamic severity of coronary stenoses when taking fractional flow reserve as the gold standard. Int J Cardiovasc Imaging. 2002;18(2):73-6. DOI:10.1023/A:1014638917413
18. Taylor CA, Fonte TA, Min JK. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. J Am Coll Cardiol. 2013;61(22):2233-41. DOI:10.1016/J.JACC.2012.11.083
19. Cook CM, Petraco R, Shun-Shin MJ, et al. Diagnostic accuracy of computed tomography-derived fractional flow reserve: a systematic review. JAMA cardiol.
2017;2(7):803-10. DOI:10.1001/jamacardio.2017.1314
20. Nørgaard BL, Fairbairn TA, Safian RD, et al. Coronary CT Angiography-derived Fractional Flow Reserve Testing in Patients with Stable Coronary Artery Disease: Recommendations on Interpretation and Reporting. Radiol Cardiothorac Imaging. 2019;1(5):e190050. DOI:10.1148/ryct.2019190050
21. Lell MM, Kachelrieß M. Recent and upcoming technological developments in computed tomography: high speed, low dose, deep learning, multienergy. Invest Radiol.
2020;55(1):8-19. DOI:10.1007/S00330-019-06183-Y
22. van Hamersvelt RW, Zreik M, Voskuil M, et al. Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis. Eur Radiol. 2019;29:2350-9. DOI:10.1007/s00330-018-5822-3
23. Zreik M, van Hamersvelt RW, Khalili N, et al. Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography. IEEE Trans Med Imaging. 2019;39(5):1545-57. DOI:10.1109/TMI.2019.2953054
24. Wang ZQ, Zhou YJ, Zhao YX, et al. Diagnostic accuracy of a deep learning approach to calculate FFR from coronary CT angiography. J Geriatr Cardiol. 2019;16(1):42-8. DOI:10.11909/j.issn.1671-5411.2019.01.010
25. Tesche C, De Cecco CN, Baumann S, et al. Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling. Radiology. 2018;288(1):64-72. DOI:10.1148/radiol.2018171291
26. Coenen A, Kim YH, Kruk M, et al. Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE consortium. Circulation: Cardiovasc Imaging. 2018;11(6):e007217. DOI:10.1161/CIRCIMAGING.117.007217
27. Василевский Ю.В., Гамилов Т.М., Симаков С.С., и др. Вычислительная технология неинвазивной диагностики стенозов коронарных артерий пациентов с ишемической болезнью сердца. Биотехнология: состояние и перспективы развития: материалы международного конгресса. Москва, 26–29 октября 2021 года. М.: Экспо-биохим-технологии, 2021; c. 132-3 [Vasilevskii IuV, Gamilov TM, Simakov SS, et al. Computational technology for non-invasive diagnosis of coronary artery stenosis in patients with ischemic heart disease. Biotechnology: state of the art and perspectives. Moskva, 26–29 oktiabria 2021 goda. Moscow: Ekspo-biokhim-tekhnologii, 2021; p. 132-3 (in Russian)]. DOI:10.37747/2312-640X-2021-19-132-133
28. Simakov SS, Gamilov TM, Liang F, et al. Numerical evaluation of the effectiveness of coronary revascularization. Russian Journal of Numerical Analysis and Mathematical Modelling. 2021;36(5):303-12. DOI:10.1515/rnam-2021-0025
29. Klüner LV, Oikonomou EK, Antoniades C. Assessing cardiovascular risk by using the fat attenuation index in coronary CT angiography. Radiology: Cardiovasc Imaging. 2021;3(1):e200563. DOI:10.1148/RYCT.2021200563
30. Ngam PI, Ong CC, Chai P, et al. Computed tomography coronary angiography – past, present and future. Singapore Med J. 2020;61(3):109. DOI:10.11622/SMEDJ.2020028
31. Magalhães TA, Kishi S, George RT, et al. Combined coronary angiography and myocardial perfusion by computed tomography in the identification of flow-limiting stenosis – the CORE320 study: an integrated analysis of CT coronary angiography and myocardial perfusion. J Cardiovasc Comput Tomogr. 2015;9:438-45. DOI:10.1016/j.jcct.2015.03.004
32. Rajiah P, Abbara S, Halliburton SS. Spectral detector CT for cardiovascular applications. Diagn Interv Radiol. 2017;23(3):187. DOI:10.5152/dir.2016.16255
33. Taguchi K, Iwanczyk JS. Vision 20/20: Single photon counting x-ray detectors in medical imaging. Med Phys. 2013;40(10):100901. DOI:10.1118/1.4820371
34. Si-Mohamed SA, Boccalini S, Lacombe H, et al. Coronary CT angiography with photon-counting CT: first-in-human results. Radiology. 2022;211780. DOI:10.1148/radiol.211780
2. Frost EB. Experiments on the X-rays. Science. 1896;3(59):235-6.
3. Radon J. Uber die Bestimmung von Funktionen durch ihre Integralwerte langs gewisser Mannigfaltigkeiten. Berichte Saechsische Akademie der Wissenshcafien. 1917;69:262-79.
4. Cormack AM. Representation of a function by its line integrals, with some radiological applications. J Appl Phys. 1963;34(9):2722-7.
5. Hounsfield GN. Method of an apparatus for examining a body by radiation such as x or gamma radiation. Instrumentation related to nuclear science and technology (e4100). 1975.
6. Ternovoy SK, Sinitsyn VE. Prospects for the development of radiation diagnostic methods. Analytical review. 2007.Available at: https://rosoncoweb.ru/library/radiodiagnostics/002.php. Accessed: 06.06.2022 (in Russian).
7. Haschek E, Lindenthal OT. Ein Beitrag zur praktischen verwerthung der photographie nach rontgen. Wien Klin Wochenschr. 1896;9:63-4.
8. Heuser C. A Chair for Radio-Therapeutic Treatment of the Perineum in a Seated Position. Br J Radiol: BIR Section. 1925;30(296):109-13.
9. Burnett HF, Parnell CL, Williams GD, Campbell GS. Peripheral arterial injuries: a reassessment. Ann Surg. 1976;183(6):701. DOI:10.1097/00000658-197606000-00014
10. Berk ME. Arteriography in peripheral trauma. Clin Radiol. 1963;14(2):235-9. DOI:10.1016/S0009-9260(63)80094-X
11. Forssmann W. Die sondierung des rechten herzens. Klinische Wochenschrift. 1929;8(45):2085-7.
12. Mason Sones F. Cine-Cardio-Angiography. Pediatric Clinics of North America. 1958;5(4):945-79. DOI:10.1016/S0031-3955(16)30724-6
13. Kalender WA, Seissler W, Klotz E, Vock P. Spiral volumetric CT with single-breath-hold technique, continuous transport, and continuous scanner rotation. Radiology.
1990;176(1):181-3. DOI:10.1148/radiology.176.1.2353088
14. Knuuti J, Wijns W, Saraste A, et al. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes: The Task Force for the diagnosis and management of chronic coronary syndromes of the European Society of Cardiology (ESC). Eur Heart J. 2020;41(3):407-77. DOI:10.1093/eurheartj/ehz425
15. Gulati M, Levy P, Mukherjee D, et al. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain. J Am Coll Cardiol.
2021;78(22):e187-285. DOI:10.1016/j.jacc.2021.07.053
16. De Bruyne B, Sarma J. Fractional flow reserve: a review. Heart. 2008;94(7):949-59. DOI:10.1136/hrt.2007.122838
17. Brueren BRG, Ten Berg JM, Suttorp MJ, et al. How good are experienced cardiologists at predicting the hemodynamic severity of coronary stenoses when taking fractional flow reserve as the gold standard. Int J Cardiovasc Imaging. 2002;18(2):73-6. DOI:10.1023/A:1014638917413
18. Taylor CA, Fonte TA, Min JK. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. J Am Coll Cardiol. 2013;61(22):2233-41. DOI:10.1016/J.JACC.2012.11.083
19. Cook CM, Petraco R, Shun-Shin MJ, et al. Diagnostic accuracy of computed tomography-derived fractional flow reserve: a systematic review. JAMA cardiol.
2017;2(7):803-10. DOI:10.1001/jamacardio.2017.1314
20. Nørgaard BL, Fairbairn TA, Safian RD, et al. Coronary CT Angiography-derived Fractional Flow Reserve Testing in Patients with Stable Coronary Artery Disease: Recommendations on Interpretation and Reporting. Radiol Cardiothorac Imaging. 2019;1(5):e190050. DOI:10.1148/ryct.2019190050
21. Lell MM, Kachelrieß M. Recent and upcoming technological developments in computed tomography: high speed, low dose, deep learning, multienergy. Invest Radiol.
2020;55(1):8-19. DOI:10.1007/S00330-019-06183-Y
22. van Hamersvelt RW, Zreik M, Voskuil M, et al. Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis. Eur Radiol. 2019;29:2350-9. DOI:10.1007/s00330-018-5822-3
23. Zreik M, van Hamersvelt RW, Khalili N, et al. Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography. IEEE Trans Med Imaging. 2019;39(5):1545-57. DOI:10.1109/TMI.2019.2953054
24. Wang ZQ, Zhou YJ, Zhao YX, et al. Diagnostic accuracy of a deep learning approach to calculate FFR from coronary CT angiography. J Geriatr Cardiol. 2019;16(1):42-8. DOI:10.11909/j.issn.1671-5411.2019.01.010
25. Tesche C, De Cecco CN, Baumann S, et al. Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling. Radiology. 2018;288(1):64-72. DOI:10.1148/radiol.2018171291
26. Coenen A, Kim YH, Kruk M, et al. Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE consortium. Circulation: Cardiovasc Imaging. 2018;11(6):e007217. DOI:10.1161/CIRCIMAGING.117.007217
27. Vasilevskii IuV, Gamilov TM, Simakov SS, et al. Computational technology for non-invasive diagnosis of coronary artery stenosis in patients with ischemic heart disease. Biotechnology: state of the art and perspectives. Moskva, 26–29 oktiabria 2021 goda. Moscow: Ekspo-biokhim-tekhnologii, 2021; p. 132-3 (in Russian).
DOI:10.37747/2312-640X-2021-19-132-133
28. Simakov SS, Gamilov TM, Liang F, et al. Numerical evaluation of the effectiveness of coronary revascularization. Russian Journal of Numerical Analysis and Mathematical Modelling. 2021;36(5):303-12. DOI:10.1515/rnam-2021-0025
29. Klüner LV, Oikonomou EK, Antoniades C. Assessing cardiovascular risk by using the fat attenuation index in coronary CT angiography. Radiology: Cardiovasc Imaging. 2021;3(1):e200563. DOI:10.1148/RYCT.2021200563
30. Ngam PI, Ong CC, Chai P, et al. Computed tomography coronary angiography – past, present and future. Singapore Med J. 2020;61(3):109. DOI:10.11622/SMEDJ.2020028
31. Magalhães TA, Kishi S, George RT, et al. Combined coronary angiography and myocardial perfusion by computed tomography in the identification of flow-limiting stenosis – the CORE320 study: an integrated analysis of CT coronary angiography and myocardial perfusion. J Cardiovasc Comput Tomogr. 2015;9:438-45. DOI:10.1016/j.jcct.2015.03.004
32. Rajiah P, Abbara S, Halliburton SS. Spectral detector CT for cardiovascular applications. Diagn Interv Radiol. 2017;23(3):187. DOI:10.5152/dir.2016.16255
33. Taguchi K, Iwanczyk JS. Vision 20/20: Single photon counting x-ray detectors in medical imaging. Med Phys. 2013;40(10):100901. DOI:10.1118/1.4820371
34. Si-Mohamed SA, Boccalini S, Lacombe H, et al. Coronary CT angiography with photon-counting CT: first-in-human results. Radiology. 2022;211780. DOI:10.1148/radiol.211780
2. Frost EB. Experiments on the X-rays. Science. 1896;3(59):235-6.
3. Radon J. Uber die Bestimmung von Funktionen durch ihre Integralwerte langs gewisser Mannigfaltigkeiten. Berichte Saechsische Akademie der Wissenshcafien. 1917;69:262-79.
4. Cormack AM. Representation of a function by its line integrals, with some radiological applications. J Appl Phys. 1963;34(9):2722-7.
5. Hounsfield GN. Method of an apparatus for examining a body by radiation such as x or gamma radiation. Instrumentation related to nuclear science and technology (e4100). 1975.
6. Терновой С.К., Синицын В.Е. Перспективы развития методов лучевой диагностики. Аналитический обзор. 2007. Режим доступа: https://rosoncoweb.ru/library/radiodiagnostics/002.php. Ссылка aктивна на 06.06.2022 [Ternovoy SK, Sinitsyn VE. Prospects for the development of radiation diagnostic methods. Analytical review. 2007.Available at: https://rosoncoweb.ru/library/radiodiagnostics/002.php. Accessed: 06.06.2022 (in Russian)].
7. Haschek E, Lindenthal OT. Ein Beitrag zur praktischen verwerthung der photographie nach rontgen. Wien Klin Wochenschr. 1896;9:63-4.
8. Heuser C. A Chair for Radio-Therapeutic Treatment of the Perineum in a Seated Position. Br J Radiol: BIR Section. 1925;30(296):109-13.
9. Burnett HF, Parnell CL, Williams GD, Campbell GS. Peripheral arterial injuries: a reassessment. Ann Surg. 1976;183(6):701. DOI:10.1097/00000658-197606000-00014
10. Berk ME. Arteriography in peripheral trauma. Clin Radiol. 1963;14(2):235-9. DOI:10.1016/S0009-9260(63)80094-X
11. Forssmann W. Die sondierung des rechten herzens. Klinische Wochenschrift. 1929;8(45):2085-7.
12. Mason Sones F. Cine-Cardio-Angiography. Pediatric Clinics of North America. 1958;5(4):945-79. DOI:10.1016/S0031-3955(16)30724-6
13. Kalender WA, Seissler W, Klotz E, Vock P. Spiral volumetric CT with single-breath-hold technique, continuous transport, and continuous scanner rotation. Radiology.
1990;176(1):181-3. DOI:10.1148/radiology.176.1.2353088
14. Knuuti J, Wijns W, Saraste A, et al. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes: The Task Force for the diagnosis and management of chronic coronary syndromes of the European Society of Cardiology (ESC). Eur Heart J. 2020;41(3):407-77. DOI:10.1093/eurheartj/ehz425
15. Gulati M, Levy P, Mukherjee D, et al. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain. J Am Coll Cardiol.
2021;78(22):e187-285. DOI:10.1016/j.jacc.2021.07.053
16. De Bruyne B, Sarma J. Fractional flow reserve: a review. Heart. 2008;94(7):949-59. DOI:10.1136/hrt.2007.122838
17. Brueren BRG, Ten Berg JM, Suttorp MJ, et al. How good are experienced cardiologists at predicting the hemodynamic severity of coronary stenoses when taking fractional flow reserve as the gold standard. Int J Cardiovasc Imaging. 2002;18(2):73-6. DOI:10.1023/A:1014638917413
18. Taylor CA, Fonte TA, Min JK. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. J Am Coll Cardiol. 2013;61(22):2233-41. DOI:10.1016/J.JACC.2012.11.083
19. Cook CM, Petraco R, Shun-Shin MJ, et al. Diagnostic accuracy of computed tomography-derived fractional flow reserve: a systematic review. JAMA cardiol.
2017;2(7):803-10. DOI:10.1001/jamacardio.2017.1314
20. Nørgaard BL, Fairbairn TA, Safian RD, et al. Coronary CT Angiography-derived Fractional Flow Reserve Testing in Patients with Stable Coronary Artery Disease: Recommendations on Interpretation and Reporting. Radiol Cardiothorac Imaging. 2019;1(5):e190050. DOI:10.1148/ryct.2019190050
21. Lell MM, Kachelrieß M. Recent and upcoming technological developments in computed tomography: high speed, low dose, deep learning, multienergy. Invest Radiol.
2020;55(1):8-19. DOI:10.1007/S00330-019-06183-Y
22. van Hamersvelt RW, Zreik M, Voskuil M, et al. Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis. Eur Radiol. 2019;29:2350-9. DOI:10.1007/s00330-018-5822-3
23. Zreik M, van Hamersvelt RW, Khalili N, et al. Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography. IEEE Trans Med Imaging. 2019;39(5):1545-57. DOI:10.1109/TMI.2019.2953054
24. Wang ZQ, Zhou YJ, Zhao YX, et al. Diagnostic accuracy of a deep learning approach to calculate FFR from coronary CT angiography. J Geriatr Cardiol. 2019;16(1):42-8. DOI:10.11909/j.issn.1671-5411.2019.01.010
25. Tesche C, De Cecco CN, Baumann S, et al. Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling. Radiology. 2018;288(1):64-72. DOI:10.1148/radiol.2018171291
26. Coenen A, Kim YH, Kruk M, et al. Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE consortium. Circulation: Cardiovasc Imaging. 2018;11(6):e007217. DOI:10.1161/CIRCIMAGING.117.007217
27. Василевский Ю.В., Гамилов Т.М., Симаков С.С., и др. Вычислительная технология неинвазивной диагностики стенозов коронарных артерий пациентов с ишемической болезнью сердца. Биотехнология: состояние и перспективы развития: материалы международного конгресса. Москва, 26–29 октября 2021 года. М.: Экспо-биохим-технологии, 2021; c. 132-3 [Vasilevskii IuV, Gamilov TM, Simakov SS, et al. Computational technology for non-invasive diagnosis of coronary artery stenosis in patients with ischemic heart disease. Biotechnology: state of the art and perspectives. Moskva, 26–29 oktiabria 2021 goda. Moscow: Ekspo-biokhim-tekhnologii, 2021; p. 132-3 (in Russian)]. DOI:10.37747/2312-640X-2021-19-132-133
28. Simakov SS, Gamilov TM, Liang F, et al. Numerical evaluation of the effectiveness of coronary revascularization. Russian Journal of Numerical Analysis and Mathematical Modelling. 2021;36(5):303-12. DOI:10.1515/rnam-2021-0025
29. Klüner LV, Oikonomou EK, Antoniades C. Assessing cardiovascular risk by using the fat attenuation index in coronary CT angiography. Radiology: Cardiovasc Imaging. 2021;3(1):e200563. DOI:10.1148/RYCT.2021200563
30. Ngam PI, Ong CC, Chai P, et al. Computed tomography coronary angiography – past, present and future. Singapore Med J. 2020;61(3):109. DOI:10.11622/SMEDJ.2020028
31. Magalhães TA, Kishi S, George RT, et al. Combined coronary angiography and myocardial perfusion by computed tomography in the identification of flow-limiting stenosis – the CORE320 study: an integrated analysis of CT coronary angiography and myocardial perfusion. J Cardiovasc Comput Tomogr. 2015;9:438-45. DOI:10.1016/j.jcct.2015.03.004
32. Rajiah P, Abbara S, Halliburton SS. Spectral detector CT for cardiovascular applications. Diagn Interv Radiol. 2017;23(3):187. DOI:10.5152/dir.2016.16255
33. Taguchi K, Iwanczyk JS. Vision 20/20: Single photon counting x-ray detectors in medical imaging. Med Phys. 2013;40(10):100901. DOI:10.1118/1.4820371
34. Si-Mohamed SA, Boccalini S, Lacombe H, et al. Coronary CT angiography with photon-counting CT: first-in-human results. Radiology. 2022;211780. DOI:10.1148/radiol.211780
________________________________________________
2. Frost EB. Experiments on the X-rays. Science. 1896;3(59):235-6.
3. Radon J. Uber die Bestimmung von Funktionen durch ihre Integralwerte langs gewisser Mannigfaltigkeiten. Berichte Saechsische Akademie der Wissenshcafien. 1917;69:262-79.
4. Cormack AM. Representation of a function by its line integrals, with some radiological applications. J Appl Phys. 1963;34(9):2722-7.
5. Hounsfield GN. Method of an apparatus for examining a body by radiation such as x or gamma radiation. Instrumentation related to nuclear science and technology (e4100). 1975.
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Авторы
О.Ю. Миронова*, Г.О. Исаев, М.В. Бердышева, В.В. Фомин
ФГАОУ ВО «Первый Московский государственный медицинский университет им. И.М. Сеченова» Минздрава России (Сеченовский Университет), Москва, Россия
*mironova_o_yu@staff.sechenov.ru
Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
*mironova_o_yu@staff.sechenov.ru
ФГАОУ ВО «Первый Московский государственный медицинский университет им. И.М. Сеченова» Минздрава России (Сеченовский Университет), Москва, Россия
*mironova_o_yu@staff.sechenov.ru
________________________________________________
Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
*mironova_o_yu@staff.sechenov.ru
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