European Radiology

, Volume 29, Issue 6, pp 3017–3026 | Cite as

Fractional flow reserve derived from CCTA may have a prognostic role in myocardial bridging

  • Fan Zhou
  • Chun Xiang Tang
  • U. Joseph Schoepf
  • Christian Tesche
  • Maximilian J. Bauer
  • Brian E. Jacobs
  • Chang Sheng Zhou
  • Jing Yan
  • Meng Jie Lu
  • Guang Ming Lu
  • Long Jiang ZhangEmail author



To evaluate the feasibility of fractional flow reserve (cFFR) derivation from coronary CT angiography (CCTA) in patients with myocardial bridging (MB), its relationship with MB anatomical features, and clinical relevance.


This retrospective study included 120 patients with MB of the left anterior descending artery (LAD) and 41 controls. MB location, length, depth, muscle index, instance, and stenosis rate were measured. cFFR values were compared between superficial MB (≤ 2 mm), deep MB (> 2 mm), and control groups. Factors associated with abnormal cFFR values (≤ 0.80) were analyzed.


MB patients demonstrated lower cFFR values in MB and distal segments than controls (all p < 0.05). A significant cFFR difference was only found in the MB segment during systole between superficial (0.94, 0.90–0.96) and deep MB (0.91, 0.83–0.95) (p = 0.018). Abnormal cFFR values were found in 69 (57.5%) MB patients (29 [49.2%] superficial vs. 40 [65.6%] deep; p = 0.069). MB length (OR = 1.06, 95% CI 1.03–1.10; p = 0.001) and systolic stenosis (OR = 1.04, 95% CI 1.01–1.07; p = 0.021) were the main predictors for abnormal cFFR, with an area under the curve of 0.774 (95% CI 0.689–0.858; p < 0.001). MB patients with abnormal cFFR reported more typical angina (18.8% vs 3.9%, p = 0.023) than patients with normal values.


MB patients showed lower cFFR values than controls. Abnormal cFFR values have a positive association with symptoms of typical angina. MB length and systolic stenosis demonstrate moderate predictive value for an abnormal cFFR value.

Key Points

• MB patients showed lower cFFR values than controls.

• Abnormal cFFR values have a positive association with typical angina symptoms.

• MB length and systolic stenosis demonstrate moderate predictive value for an abnormal cFFR value .


Computed tomography angiography Fractional flow reserve Myocardial bridging 



Area under the curve


Coronary artery disease


Coronary computed tomography angiography


Computational fluid dynamics


CCTA-derived fractional flow reserve




Left anterior descending coronary artery


Myocardial bridging


Odds ratio


Receiver operating characteristic



This study has received funding by The National Key Research and Development Program of China (2017YFC0113400 for L.J.Z.).

Compliance with ethical standards


The scientific guarantor of this publication is Long Jiang Zhang.

Conflict of interest

UJS is a consultant for and/or receives research support from Astellas, Bayer, General Electric, Guerbet, HeartFlow, and Siemens Healthineers. The other authors have no conflicts of interest to disclose.

Statistics and biometry

Meng Jie Lu has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• observational

• performed at one institution


  1. 1.
    Forsdahl SH, Rogers IS, Schnittger I et al (2017) Myocardial bridges on coronary computed tomography angiography—correlation with intravascular ultrasound and fractional flow reserve. Circ J 81:1894–1900CrossRefGoogle Scholar
  2. 2.
    Nakanishi R, Rajani R, Ishikawa Y, Ishii T, Berman DS (2012) Myocardial bridging on coronary CTA: an innocent bystander or a culprit in myocardial infarction? J Cardiovasc Comput Tomogr 6:3–13CrossRefGoogle Scholar
  3. 3.
    Dimitriu-Leen AC, van Rosendael AR, Smit JM et al (2017) Long-term prognosis of patients with intramural course of coronary arteries assessed with CT angiography. JACC Cardiovasc Imaging 10:1451–1458CrossRefGoogle Scholar
  4. 4.
    Rihal C, Ammash N (2017) Intramural course of coronary arteries: a bridge too far no more. JACC Cardiovasc Imaging 10:1459–1460CrossRefGoogle Scholar
  5. 5.
    Rubinshtein R, Gaspar T, Lewis BS, Prasad A, Peled N, Halon DA (2013) Long-term prognosis and outcome in patients with a chest pain syndrome and myocardial bridging: a 64-slice coronary computed tomography angiography study. Eur Heart J Cardiovasc Imaging 14:579–585CrossRefGoogle Scholar
  6. 6.
    Li Y, Yu M, Zhang J, Li M, Lu Z, Wei M (2017) Non-invasive imaging of myocardial bridge by coronary computed tomography angiography: the value of transluminal attenuation gradient to predict significant dynamic compression. Eur Radiol 27:1971–1979CrossRefGoogle Scholar
  7. 7.
    Corban MT, Hung OY, Eshtehardi P et al (2014) Myocardial bridging: contemporary understanding of pathophysiology with implications for diagnostic and therapeutic strategies. J Am Coll Cardiol 63:2346–2355CrossRefGoogle Scholar
  8. 8.
    Tarantini G, Migliore F, Cademartiri F, Fraccaro C, Iliceto S (2016) Left anterior descending artery myocardial bridging: a clinical approach. J Am Coll Cardiol 68:2887–2899CrossRefGoogle Scholar
  9. 9.
    Wang Y, Lv B, Chen J et al (2013) Intramural coronary arterial course is associated with coronary arterial stenosis and prognosis of major cardiac events. Arterioscler Thromb Vasc Biol 33:439–444CrossRefGoogle Scholar
  10. 10.
    Ishikawa Y, Akasaka Y, Suzuki K et al (2009) Anatomic properties of myocardial bridge predisposing to myocardial infarction. Circulation 120:376–383CrossRefGoogle Scholar
  11. 11.
    Leschka S, Koepfli P, Husmann L et al (2008) Myocardial bridging: depiction rate and morphology at CT coronary angiography—comparison with conventional coronary angiography. Radiology 246:754–762CrossRefGoogle Scholar
  12. 12.
    Kim PJ, Hur G, Kim SY et al (2009) Frequency of myocardial bridges and dynamic compression of epicardial coronary arteries: a comparison between computed tomography and invasive coronary angiography. Circulation 119:1408–1416CrossRefGoogle Scholar
  13. 13.
    Konen E, Goitein O, Sternik L, Eshet Y, Shemesh J, Di Segni E (2007) The prevalence and anatomical patterns of intramuscular coronary arteries: a coronary computed tomography angiographic study. J Am Coll Cardiol 49:587–593CrossRefGoogle Scholar
  14. 14.
    Gould KL, Johnson NP (2015) Myocardial bridges: lessons in clinical coronary pathophysiology. JACC Cardiovasc Imaging 8:705–709CrossRefGoogle Scholar
  15. 15.
    Tarantini G, Barioli A, Nai Fovino L et al (2018) Unmasking myocardial bridge–related ischemia by intracoronary functional evaluation. Circ Cardiovasc Interv 11:e006247CrossRefGoogle Scholar
  16. 16.
    Kurata A, Coenen A, Lubbers MM et al (2017) The effect of blood pressure on non-invasive fractional flow reserve derived from coronary computed tomography angiography. Eur Radiol 27:1416–1423CrossRefGoogle Scholar
  17. 17.
    Lee HJ, Hong YJ, Kim HY et al (2012) Anomalous origin of the right coronary artery from the left coronary sinus with an interarterial course: subtypes and clinical importance. Radiology 262:101–108CrossRefGoogle Scholar
  18. 18.
    Liu SH, Yang Q, Chen JH, Wang XM, Wang M, Liu C (2010) Myocardial bridging on dual-source computed tomography: degree of systolic compression of mural coronary artery correlating with length and depth of the myocardial bridge. Clin Imaging 34:83–88CrossRefGoogle Scholar
  19. 19.
    Zhang LJ, Wang Y, Schoepf UJ et al (2016) Image quality, radiation dose, and diagnostic accuracy of prospectively ECG-triggered high-pitch coronary CT angiography at 70 kVp in a clinical setting: comparison with invasive coronary angiography. Eur Radiol 26:797–806CrossRefGoogle Scholar
  20. 20.
    Duguay TM, Tesche C, Vliegenthart R et al (2017) Coronary computed tomographic angiography-derived fractional flow reserve based on machine learning for risk stratification of non-culprit coronary narrowings in patients with acute coronary syndrome. Am J Cardiol 120:1260–1266CrossRefGoogle Scholar
  21. 21.
    Solecki M, Kruk M, Demkow M et al (2017) What is the optimal anatomic location for coronary artery pressure measurement at CT-derived FFR? J Cardiovasc Comput Tomogr 11:397–403CrossRefGoogle Scholar
  22. 22.
    Itu L, Rapaka S, Passerini T et al (2016) A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol (1985) 121:42–52CrossRefGoogle Scholar
  23. 23.
    Tesche C, De Cecco CN, Baumann S et al (2018) Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling. Radiology 288:64–72CrossRefGoogle Scholar
  24. 24.
    Kruk M, Wardziak Ł, Demkow M et al (2016) Workstation-based calculation of CTA-based FFR for intermediate stenosis. JACC Cardiovasc Imaging 9:690–699CrossRefGoogle Scholar
  25. 25.
    Tonino PA, De Bruyne B, Pijls NH et al (2009) Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. N Engl J Med 360:213–224CrossRefGoogle Scholar
  26. 26.
    Collet C, Miyazaki Y, Ryan N et al (2018) Fractional flow reserve derived from computed tomographic angiography in patients with multivessel CAD. J Am Coll Cardiol 71:2756–2769CrossRefGoogle Scholar
  27. 27.
    Tesche C, De Cecco CN, Albrecht MH et al (2017) Coronary CT angiography-derived fractional flow reserve. Radiology 285:17–33CrossRefGoogle Scholar
  28. 28.
    DeLong ER, DeLong DM, Clarke-Pearson DL (1998) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefGoogle Scholar
  29. 29.
    Escaned J, Cortés J, Flores A et al (2003) Importance of diastolic fractional flow reserve and dobutamine challenge in physiologic assessment of myocardial bridging. J Am Coll Cardiol 42:226–233CrossRefGoogle Scholar
  30. 30.
    Halliburton SS, Abbara S, Chen MY et al (2011) SCCT guidelines on radiation dose and dose optimization strategies in cardiovascular CT. J Cardiovasc Comput Tomogr 5:198–224CrossRefGoogle Scholar
  31. 31.
    Agrawal H, Molossi S, Alam M et al (2017) Anomalous coronary arteries and myocardial bridges: risk stratification in children using novel cardiac catheterization techniques. Pediatr Cardiol 38:624–630CrossRefGoogle Scholar
  32. 32.
    Takx RAP, Celeng C, Schoepf UJ (2018) CT myocardial perfusion imaging: ready for prime time? Eur Radiol 28:1253–1256CrossRefGoogle Scholar
  33. 33.
    Dey D, Gaur S, Ovrehus KA et al (2018) Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study. Eur Radiol 28:2655–2664CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Fan Zhou
    • 1
  • Chun Xiang Tang
    • 1
  • U. Joseph Schoepf
    • 1
    • 2
  • Christian Tesche
    • 2
    • 3
  • Maximilian J. Bauer
    • 2
  • Brian E. Jacobs
    • 2
  • Chang Sheng Zhou
    • 1
  • Jing Yan
    • 4
  • Meng Jie Lu
    • 1
  • Guang Ming Lu
    • 1
  • Long Jiang Zhang
    • 1
    Email author
  1. 1.Department of Medical Imaging, Jinling HospitalMedical School of Nanjing UniversityNanjingChina
  2. 2.Division of Cardiovascular Imaging, Department of Radiology and Radiological ScienceMedical University of South CarolinaCharlestonUSA
  3. 3.Department of Cardiology and Intensive Care MedicineHeart Center Munich-BogenhausenMunichGermany
  4. 4.Siemens Healthcare Ltd.ShanghaiChina

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