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CT Angiography-Derived Fractional Flow Reserve

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CT of the Heart

Part of the book series: Contemporary Medical Imaging ((CMI))

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Abstract

A virtual fractional flow reserve can be calculated from regular CT angiograms using computational fluid dynamics. Several CT-FFR applications, at a variable state of development, allow for assessment of the hemodynamic severity of coronary artery disease, limit false-positive CTA interpretations, potentially substitute other functional tests, and avoid normal invasive angiography results.

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References

  1. Authors/Task Force members, Windecker S, Kolh P, Alfonso F, Collet JP, Cremer J, et al. 2014 ESC/EACTS guidelines on myocardial revascularization: the task force on myocardial revascularization of the European Society of Cardiology (ESC) and the European Association for Cardio-Thoracic Surgery (EACTS) developed with the special contribution of the European Association of Percutaneous Cardiovascular Interventions (EAPCI). Eur Heart J. 2014;35(37):2541–619.

    Article  Google Scholar 

  2. Levine GN, Bates ER, Blankenship JC, Bailey SR, Bittl JA, Cercek B, et al. 2011 ACCF/AHA/SCAI guideline for percutaneous coronary intervention: a report of the American College of Cardiology Foundation/American Heart Association task force on practice guidelines and the Society for Cardiovascular Angiography and Interventions. Circulation. 2011;124(23):e574–651.

    PubMed  Google Scholar 

  3. 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.

    Article  PubMed  Google Scholar 

  4. Morris PD, Narracott A, von Tengg-Kobligk H, Silva Soto DA, Hsiao S, Lungu A, et al. Computational fluid dynamics modelling in cardiovascular medicine. Heart. 2016;102(1):18–28.

    Article  PubMed  Google Scholar 

  5. Min JK, Taylor CA, Achenbach S, Koo BK, Leipsic J, Norgaard BL, et al. Noninvasive fractional flow reserve derived from coronary CT angiography: clinical data and scientific principles. JACC Cardiovasc Imaging. 2015;8(10):1209–22.

    Article  PubMed  Google Scholar 

  6. Schaap M, van Walsum T, Neefjes L, Metz C, Capuano E, de Bruijne M, et al. Robust shape regression for supervised vessel segmentation and its application to coronary segmentation in CTA. IEEE Trans Med Imaging. 2011;30(11):1974–86.

    Article  PubMed  Google Scholar 

  7. Choy JS, Kassab GS. Scaling of myocardial mass to flow and morphometry of coronary arteries. J Appl Physiol. 2008;104(5):1281–6.

    Article  PubMed  Google Scholar 

  8. Coenen A, Lubbers MM, Kurata A, Kono A, Dedic A, Chelu RG, et al. Coronary CT angiography derived fractional flow reserve: methodology and evaluation of a point of care algorithm. J Cardiovasc Comput Tomogr. 2016;10(2):105–13.

    Article  PubMed  Google Scholar 

  9. Itu L, Sharma P, Mihalef V, Kamen A, Suciu C, Lomaniciu D, editors. A patient-specific reduced-order model for coronary circulation. 2012 9th IEEE international symposium on biomedical imaging (ISBI); 2012. 2–5 May 2012.

    Google Scholar 

  10. Ko BS, Wong DT, Norgaard BL, Leong DP, Cameron JD, Gaur S, et al. Diagnostic performance of transluminal attenuation gradient and noninvasive fractional flow reserve derived from 320-detector row CT angiography to diagnose hemodynamically significant coronary stenosis: an NXT substudy. Radiology. 2016;279(1):75–83.

    Article  PubMed  Google Scholar 

  11. Wilson RF, Wyche K, Christensen BV, Zimmer S, Laxson DD. Effects of adenosine on human coronary arterial circulation. Circulation. 1990;82(5):1595–606.

    Article  CAS  PubMed  Google Scholar 

  12. Koo BK, Erglis A, Doh JH, Daniels DV, Jegere S, Kim HS, et al. Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms. Results from the prospective multicenter DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noninvasive Fractional Flow Reserve) study. J Am Coll Cardiol. 2011;58(19):1989–97.

    Article  PubMed  Google Scholar 

  13. Ko BS, Cameron JD, Munnur RK, Wong DT, Fujisawa Y, Sakaguchi T, et al. Noninvasive CT-derived FFR based on structural and fluid analysis: a comparison with invasive FFR for detection of functionally significant stenosis. JACC Cardiovasc Imaging. 2017;10(6):663–73.

    Article  PubMed  Google Scholar 

  14. Nickisch H, Lamash Y, Prevrhal S, Freiman M, Vembar M, Goshen L, et al. Learning patient-specific lumped models for interactive coronary blood flow simulations. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Medical image computing and computer-assisted intervention – MICCAI 2015: 18th international conference, Munich, Germany, October 5–9, 2015, proceedings, part II. Cham: Springer International Publishing; 2015. p. 433–41.

    Chapter  Google Scholar 

  15. Itu L, Rapaka S, Passerini T, Georgescu B, Schwemmer C, Schoebinger M, et al. A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol. 2016;121(1):42–52.

    Article  PubMed  Google Scholar 

  16. Min JK, Leipsic J, Pencina MJ, Berman DS, Koo BK, van Mieghem C, et al. Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA. 2012;308(12):1237–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Norgaard BL, Leipsic J, Gaur S, Seneviratne S, Ko BS, Ito H, et al. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease the NXT trial (analysis of coronary blood flow using CT angiography: next steps). J Am Coll Cardiol. 2014;63(12):1145–55.

    Article  PubMed  Google Scholar 

  18. Douglas PS, Pontone G, Hlatky MA, Patel MR, Norgaard BL, Byrne RA, et al. Clinical outcomes of fractional flow reserve by computed tomographic angiography-guided diagnostic strategies vs. usual care in patients with suspected coronary artery disease: the prospective longitudinal trial of FFR(CT): outcome and resource impacts study. Eur Heart J. 2015;36(47):3359–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Douglas PS, De Bruyne B, Pontone G, Patel MR, Norgaard BL, Byrne RA, et al. 1-year outcomes of FFRCT-guided care in patients with suspected coronary disease: the PLATFORM study. J Am Coll Cardiol. 2016;68(5):435–45.

    Article  PubMed  Google Scholar 

  20. Renker M, Schoepf UJ, Wang R, Meinel FG, Rier JD, Bayer RR, et al. Comparison of diagnostic value of a novel noninvasive coronary computed tomography angiography method versus standard coronary angiography for assessing fractional flow reserve. Am J Cardiol. 2014;114(9):1303–8.

    Article  PubMed  Google Scholar 

  21. Coenen A, Lubbers MM, Kurata A, Kono A, Dedic A, Chelu RG, et al. Fractional flow reserve computed from noninvasive CT angiography data: diagnostic performance of an on-site clinician-operated computational fluid dynamics algorithm. Radiology. 2015;274(3):674–83.

    Article  PubMed  Google Scholar 

  22. Kruk M, Wardziak L, Demkow M, Pleban W, Pregowski J, Dzielinska Z, et al. Workstation-based calculation of CTA-based FFR for intermediate stenosis. JACC Cardiovasc Imaging. 2016;9(6):690–9.

    Article  PubMed  Google Scholar 

  23. Kurata A, Coenen A, Lubbers MM, Nieman K, Kido T, Kido T, et al. The effect of blood pressure on non-invasive fractional flow reserve derived from coronary computed tomography angiography. Eur Radiol. 2017;27(4):1416–23.

    Article  PubMed  Google Scholar 

  24. De Geer J, Sandstedt M, Bjorkholm A, Alfredsson J, Janzon M, Engvall J, et al. Software-based on-site estimation of fractional flow reserve using standard coronary CT angiography data. Acta Radiol. 2016;57(10):1186–92.

    Article  PubMed  Google Scholar 

  25. Yang DH, Kim YH, Roh JH, Kang JW, Ahn JM, Kweon J, et al. Diagnostic performance of on-site CT-derived fractional flow reserve versus CT perfusion. Eur Heart J Cardiovasc Imaging. 2017;18(4):432–40.

    Article  PubMed  Google Scholar 

  26. Schrauwen JT, Koeze DJ, Wentzel JJ, van de Vosse FN, van der Steen AF, Gijsen FJ. Fast and accurate pressure-drop prediction in straightened atherosclerotic coronary arteries. Ann Biomed Eng. 2015;43(1):59–67.

    Article  PubMed  Google Scholar 

  27. Coenen A, Rossi A, Lubbers MM, Kurata A, Kono AK, Chelu RG, Segreto S, Dijkshoorn ML, Wragg A, van Geuns RM, Pugliese F, Nieman K. Integrating CT myocardial perfusion and CT-FFR in the work-up of coronary artery disease. JACC Cardiovasc Imaging. 2017;10(7):760–70.

    Article  PubMed  Google Scholar 

  28. Norgaard BL, Gaur S, Leipsic J, Ito H, Miyoshi T, Park SJ, et al. Influence of coronary calcification on the diagnostic performance of CT angiography derived FFR in coronary artery disease a substudy of the NXT trial. JACC Cardiovasc Imaging. 2015;8(9):1045–55.

    Article  PubMed  Google Scholar 

  29. Gaur S, Taylor CA, Jensen JM, Botker HE, Christiansen EH, Kaltoft AK, et al. FFR derived from coronary CT angiography in nonculprit lesions of patients with recent STEMI. JACC Cardiovasc Imaging. 2017;10(4):424–33.

    Article  PubMed  Google Scholar 

  30. Kim KH, Doh JH, Koo BK, Min JK, Erglis A, Yang HM, et al. A novel noninvasive technology for treatment planning using virtual coronary stenting and computed tomography-derived computed fractional flow reserve. JACC Cardiovasc Interv. 2014;7(1):72–8.

    Article  PubMed  Google Scholar 

  31. Glagov S, Weisenberg E, Zarins CK, Stankunavicius R, Kolettis GJ. Compensatory enlargement of human atherosclerotic coronary arteries. N Engl J Med. 1987;316(22):1371–5.

    Article  CAS  PubMed  Google Scholar 

  32. Wentzel JJ, Chatzizisis YS, Gijsen FJ, Giannoglou GD, Feldman CL, Stone PH. Endothelial shear stress in the evolution of coronary atherosclerotic plaque and vascular remodelling: current understanding and remaining questions. Cardiovasc Res. 2012;96(2):234–43.

    Article  CAS  PubMed  Google Scholar 

  33. Gijsen FJ, Wentzel JJ, Thury A, Mastik F, Schaar JA, Schuurbiers JC, et al. Strain distribution over plaques in human coronary arteries relates to shear stress. Am J Physiol Heart Circ Physiol. 2008;295(4):H1608–14.

    Article  CAS  PubMed  Google Scholar 

  34. Samady H, Eshtehardi P, McDaniel MC, Suo J, Dhawan SS, Maynard C, et al. Coronary artery wall shear stress is associated with progression and transformation of atherosclerotic plaque and arterial remodeling in patients with coronary artery disease. Circulation. 2011;124(7):779–88.

    Article  CAS  PubMed  Google Scholar 

  35. Gijsen F, van der Giessen A, van der Steen A, Wentzel J. Shear stress and advanced atherosclerosis in human coronary arteries. J Biomech. 2013;46(2):240–7.

    Article  PubMed  Google Scholar 

  36. Cicha I, Worner A, Urschel K, Beronov K, Goppelt-Struebe M, Verhoeven E, et al. Carotid plaque vulnerability: a positive feedback between hemodynamic and biochemical mechanisms. Stroke. 2011;42(12):3502–10.

    Article  CAS  PubMed  Google Scholar 

  37. Fujii K, Kobayashi Y, Mintz GS, Takebayashi H, Dangas G, Moussa I, et al. Intravascular ultrasound assessment of ulcerated ruptured plaques: a comparison of culprit and nonculprit lesions of patients with acute coronary syndromes and lesions in patients without acute coronary syndromes. Circulation. 2003;108(20):2473–8.

    Article  PubMed  Google Scholar 

  38. Zhang JM, Luo T, Tan SY, Lomarda AM, Wong AS, Keng FY, et al. Hemodynamic analysis of patient-specific coronary artery tree. Int J Numer Method Biomed Eng. 2015;31(4):e02708.

    Article  PubMed  Google Scholar 

  39. van der Giessen AG, Groen HC, Doriot PA, de Feyter PJ, van der Steen AF, van de Vosse FN, et al. The influence of boundary conditions on wall shear stress distribution in patients specific coronary trees. J Biomech. 2011;44(6):1089–95.

    Article  PubMed  Google Scholar 

  40. Koo BK. Haemodynamic force analysis improves non-invasive prediction of risk of ACS: the results of first-in-man EMERALD study (exploring the mechanism of the plaque rupture in acute coronary syndrome using coronary CT angiography and computational fluid dynamics). Imaging and functional assessment presented at: EuroPCR; May 16–19, 2016; Paris. 2016.

    Google Scholar 

  41. Chan BT, Lim E, Chee KH, Abu Osman NA. Review on CFD simulation in heart with dilated cardiomyopathy and myocardial infarction. Comput Biol Med. 2013;43(4):377–85.

    Article  PubMed  Google Scholar 

  42. Aguado-Sierra J, Krishnamurthy A, Villongco C, Chuang J, Howard E, Gonzales MJ, et al. Patient-specific modeling of dyssynchronous heart failure: a case study. Prog Biophys Mol Biol. 2011;107(1):147–55.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Lantz J, Henriksson L, Persson A, Karlsson M, Ebbers T. Patient-specific simulation of cardiac blood flow from high-resolution computed tomography. J Biomech Eng. 2016;138(12):121004.

    Article  Google Scholar 

  44. Astorino M, Hamers J, Shadden SC, Gerbeau JF. A robust and efficient valve model based on resistive immersed surfaces. Int J Numer Method Biomed Eng. 2012;28(9):937–59.

    Article  PubMed  Google Scholar 

  45. Kawaji T, Shiomi H, Morishita H, Morimoto T, Taylor CA, Kanao S, et al. Feasibility and diagnostic performance of fractional flow reserve measurement derived from coronary computed tomography angiography in real clinical practice. Int J Cardiovasc Imaging. 2017;33(2):271–81.

    Article  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Departments of Radiology and Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands

    Adriaan Coenen

  2. Department of Biomedical Engineering, Erasmus University Medical Center, Rotterdam, The Netherlands

    Frank Gijsen

  3. Stanford University, School of Medicine, Cardiovascular Institute, Stanford, CA, USA

    Koen Nieman

Authors
  1. Adriaan Coenen
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  2. Frank Gijsen
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  3. Koen Nieman
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Corresponding author

Correspondence to Koen Nieman .

Editor information

Editors and Affiliations

  1. Department of Radiology, Center for Advanced Imaging Research (CAIR), Medical University of South Carolina, Charleston, SC, USA

    U. Joseph Schoepf

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Coenen, A., Gijsen, F., Nieman, K. (2019). CT Angiography-Derived Fractional Flow Reserve. In: Schoepf, U. (eds) CT of the Heart. Contemporary Medical Imaging. Humana, Totowa, NJ. https://doi.org/10.1007/978-1-60327-237-7_60

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  • DOI: https://doi.org/10.1007/978-1-60327-237-7_60

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  • Publisher Name: Humana, Totowa, NJ

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