Computer methods for follow-up study of hemodynamic and disease progression in the stented coronary artery by fusing IVUS and X-ray angiography

  • Arso M. Vukicevic
  • Nemanja M. Stepanovic
  • Gordana R. Jovicic
  • Svetlana R. Apostolovic
  • Nenad D. Filipovic
Original Article

Abstract

Despite a lot of progress in the fields of medical imaging and modeling, problem of estimating the risk of in-stent restenosis and monitoring the progress of the therapy following stenting still remains. The principal aim of this paper was to propose architecture and implementation details of state of the art of computer methods for a follow-up study of disease progression in coronary arteries stented with bare-metal stents. The 3D reconstruction of coronary arteries was performed by fusing X-ray angiography and intravascular ultrasound (IVUS) as the most dominant modalities in interventional cardiology. The finite element simulation of plaque progression was performed by coupling the flow equations with the reaction–diffusion equation applying realistic boundary conditions at the wall. The alignment of baseline and follow-up data was performed automatically by temporal alignment of IVUS electrocardiogram-gated frames. The assessment was performed using three six-month follow-ups of right coronary artery. Simulation results were compared with the ground truth data measured by clinicians. In all three data sets, simulation results indicated the right places as critical. With the obtained difference of 5.89 ± ~4.5 % between the clinical measurements and the results of computer simulations, we showed that presented framework is suitable for tracking the progress of coronary disease, especially for comparing face-to-face results and data of the same artery from distinct time periods.

Keywords

Image-based modeling Restenosis X-ray angiography IVUS Finite element method Follow-up 

References

  1. 1.
    Alberti M, Balocco S, Carrillo X, Mauri J, Radeva P (2013) Automatic non-rigid temporal alignment of intravascular ultrasound sequences: method and quantitative validation. Ultrasound Med Biol 39(9):1698–1712PubMedCrossRefGoogle Scholar
  2. 2.
    Antiga L, Piccinelli M, Botti L, Ene-Iordache B, Remuzzi A, Steinman DA (2008) An image-based modeling framework for patient-specific computational hemodynamics. Med Biol Eng Compu 46(11):1097–1112CrossRefGoogle Scholar
  3. 3.
    Balasubramanian D, Srinivasan P, Gurupatham R (2007) Automatic classification of focal lesions in ultrasound liver images using principal component analysis and neural networks. In: Proceedings of IEEE engineering medicinal biological society on conference, Lyon, pp 2134–2137Google Scholar
  4. 4.
    Balocco S, Gatta C, Alberti M, Carrillo X, Rigla J, Radeva P (2012) Relation between plaque type, plaque thickness, blood shear stress and plaque stress in coronary arteries assessed by X-ray angiography and intravascular ultrasound. Med Phys 39(12):7430–7445PubMedCrossRefGoogle Scholar
  5. 5.
    Bathe KJ (1996) Finite element procedures. Prentice-Hall, Englewood CliffsGoogle Scholar
  6. 6.
    Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24(4):509–522CrossRefGoogle Scholar
  7. 7.
    Bourantas C, Kalatzis F, Papafaklis M, Fotiadis D, Tweddel A, Kourtis I, Katsouras C, Michalis L (2008) ANGIOCARE: an automated system for fast three-dimensional coronary reconstruction by integrating angiographic and intracoronary ultrasound data. Catheter Cardiovasc Interv 72:166–175PubMedCrossRefGoogle Scholar
  8. 8.
    Butterworth S (1930) On the theory of filter amplifiers. Wirel Eng 7:536–541Google Scholar
  9. 9.
    Caiazzo A, Evans D, Falcone JL, Hegewald J, Lorenz E, Stahl B, Wang D, Bernsdorf J, Chopard B, Gunn J, Hose R, Krafczyk M, Lawford P, Smallwood R, Hoekstra A, Walker D (2011) A complex automata approach for in-stent restenosis: two-dimensional multiscale modelling and simulations. J Comput Sci 2(1):9–17CrossRefGoogle Scholar
  10. 10.
    Cárdenes R, Díez JL, Larrabide I, Bogunovic H, Frangi AF (2011) 3D Modeling of coronary artery bifurcations from CTA and conventional coronary angiography. MICCAI; Toronto pp 395–402Google Scholar
  11. 11.
    Cárdenes R, Novikov A, Gunn J, Hose RD, Frangi AF (2012) 3D reconstruction of coronary arteries from rotational X-ray angiography. In Proceedings on international symposium on biomedical imaging, Barcelona, pp 618–621Google Scholar
  12. 12.
    Chen SJ, Carroll JD (2000) 3-D reconstruction of coronary arterial tree to optimize angiographic visualization. IEEE Trans Med Imaging 19(4):318–336PubMedCrossRefGoogle Scholar
  13. 13.
    Chen SY, Carroll JD, Messenger JC (2002) Quantitative analysis of reconstructed 3-D coronary arterial tree and intracoronary devices. IEEE Trans Med Imaging 21(7):724–740PubMedCrossRefGoogle Scholar
  14. 14.
    Chiastra C, Morlacchi S, Pereira S, Dubini G, Migliavacca F (2012) Computational fluid dynamics of stented coronary bifurcations studied with a hybrid discretization method. Eur J Mech B Fluids 35:76–84CrossRefGoogle Scholar
  15. 15.
    Chrzanowski L, Drozdz J, Strzelecki M, Krzeminska-Pakula M, Jedrzejewski K, Kasprzak J (2008) Application of neural networks for the analysis of intravascular ultrasound and histological aortic wall appearance-an in vitro tissue characterization study. Ultrasound Med Biol 34(1):103–113PubMedCrossRefGoogle Scholar
  16. 16.
    Ciompi F, Pujol O, Gatta C, Alberti M, Balocco S, Carrillo X, Mauri-Ferre J, Radeva P (2012) HoliMAb: a holistic approach for media-adventitia border detection in intravascular ultrasound. Med Image Anal 16(6):1085–1100PubMedCrossRefGoogle Scholar
  17. 17.
    Cosottini M, Michelassi MC,Bencivelli W, Lazzarotti G, Picchietti S, Orlandi G, Parenti G, Puglioli M (2010) In stent restenosis predictors after carotid artery stenting. Stroke Res Treat Article ID 864724:6. doi:10.4061/2010/864724
  18. 18.
    de Boor C (1972) On calculating with B-splines. J Approx Theory 6(1):50–62CrossRefGoogle Scholar
  19. 19.
    Dehlaghi V, Shadpoor MT, Najarian S (2008) Analysis of wall shear stress in stented coronary artery using 3D computational fluid dynamics modeling. J Mater Process Technol 197(1–3):174–181CrossRefGoogle Scholar
  20. 20.
    Donea J (1983) Arbitrary Lagrangian-Eulerian finite elements methods. In: Belytschko T, Hughes TJR (eds) Computational methods for transient analysis. Elsevier, Amsterdam, pp 473–516Google Scholar
  21. 21.
    Elizabeth G, Nabel MD, Braunwald E, Silverman MD (2012) Tale of coronary artery disease and myocardial infarction. N Engl J Med 366:54–63CrossRefGoogle Scholar
  22. 22.
    Feldman C, Ilegbusi O, Hu Z, Nesto R, Waxman S, Stone P (2002) Determination of in vivo velocity and endothelial shear stress patterns with phasic flow in human coronary arteries: a methodology to predict progression of coronary atherosclerosis. Am Heart J 143:931–939PubMedCrossRefGoogle Scholar
  23. 23.
    Filipovic N (2013) PAK-Athero, finite element program for plaque formation and development. University of Kragujevac, SerbiaGoogle Scholar
  24. 24.
    Filipovic N, Rosic M, Tanaskovic I, Milosevic Z, Nikolic D, Zdravkovic N, Peulic A, Fotiadis D, Parodi O (2011) ARTreat project: three-dimensional numerical simulation of plaque formation and development in the arteries. IEEE Trans Inf Technol Biomed 16(2):272–278PubMedCrossRefGoogle Scholar
  25. 25.
    Filipovic N, Teng Z, Radovic M, Saveljic I, Fotiadis D, Parodi O (2013) Computer simulation of three dimensional plaque formation and progression in the carotid artery. Med Biol Eng Compu 51(6):607–616CrossRefGoogle Scholar
  26. 26.
    Frenet F (1852) Sur les courbes à double courbure. Thèse, Toulouse [in French]Google Scholar
  27. 27.
    Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Borden WB, Bravata DM, Dai S, Ford ES, Fox CS, Franco S, Fullerton HJ, Gillespie C, Hailpern SM, Heit JA, Howard VJ, Huffman MD, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Magid D, Marcus GM, Marelli A, Matchar DB, McGuire DK, Mohler ER, Moy CS, Mussolino ME, Nichol G, Paynter NP, Schreiner PJ, Sorlie PD, Stein J, Turan TN, Virani SS, Wong ND, Woo D, Turner MB; American Heart Association Statistics Committee and Stroke Statistics Subcommittee (2013) Heart disease and stroke statistics-2013 update: a report from the American Heart Association. Circulation 27(1):e6–e245Google Scholar
  28. 28.
    Groher M, Hoffmann RT, Zech CJ, Reiser M, Navab N (2007) An efficient registration algorithm for advanced fusion of 2D/3D angiographic data. Bildverarbeitungfür die Medizin, Springer, Berlin, pp 156–160Google Scholar
  29. 29.
    Hartley R, Zisserman A (2004) Multiple view geometry in computer vision. Cambridge University Press, Cambridge, pp 239–261Google Scholar
  30. 30.
    Hernàndez-Sabaté A, Gil D, Fernandez-Nofrerias E, Radeva P, Martí E (2009) Approaching rigid artery dynamics in IVUS. IEEE Trans Med Imaging 28(11):1670–1680PubMedCrossRefGoogle Scholar
  31. 31.
    Hoffmann KR, Wahle A, Pellot-Barakat C, Sklansky J, Sonka M (1999) Biplane X-ray angiograms, intravascular ultrasound, and 3D visualization of coronary vessels. Int J Card Imaging 15(6):495–512PubMedCrossRefGoogle Scholar
  32. 32.
    Iliopoulos CS, Rahman MS (2008) Algorithms for computing variants of the longest common subsequence problem. Theoret Comput Sci 395(2–3):255–267CrossRefGoogle Scholar
  33. 33.
    Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vision 1(4):321–331CrossRefGoogle Scholar
  34. 34.
    Kedem O, Katchalsky A (1961) A physical interpretation of the phenomenological coefficients of membrane permeability. J Gen Physiol 45(1):143–179PubMedCentralPubMedCrossRefGoogle Scholar
  35. 35.
    Kojić M, Filipović N, Slavković R, Živković M, Grujović N (1998) PAKF: Program for FE analysis of fluid flow with heat transfer. Faculty of Mechanical Engineering Kragujevac, University of KragujevacGoogle Scholar
  36. 36.
    Koskinas K, Chatzizisis Y, Antoniadis A, Giannoglou G (2012) Role of endothelial shear stress in stent restenosis and thrombosis: pathophysiologic mechanisms and implications for clinical translation. J Am Coll Cardiol 59(15):1337–1349PubMedCrossRefGoogle Scholar
  37. 37.
    Kottke TE, Faith DA, Jordan CO, Pronk NP, Thomas RJ, Capewell S (2009) The comparative effectiveness of heart disease prevention and treatment strategies. Am J Prev Med 36(1):82–88PubMedCrossRefGoogle Scholar
  38. 38.
    Kumar M (2010) A review of coronary stents and study of its interaction with artery using finite element analysis. J Innov Res Eng Sci 1(1):134–138Google Scholar
  39. 39.
    Laban M, Oomen JA, Slager CJ, Wentzel JJ, Krams R, Schuurbiers JCH, den Beer A, von Birgelen C, Serruys PW, de Feijter PJ (1995) ANGUS: a new approach to three-dimensional reconstruction of coronary vessels by combined use of angiography and intravascular ultrasound. Computers in Cardiology, Vienna, pp 325–328Google Scholar
  40. 40.
    Laine AF (2012) A state-of-the-art review on segmentation algorithms in intravascular ultrasound (IVUS) images. IEEE Trans Inf Technol Biomed 16(5):823–834PubMedCrossRefGoogle Scholar
  41. 41.
    Lally C, Dolan F, Prendergast PJ (2005) Cardiovascular stent design and vessel stresses: a finite element analysis. J Biomech 38(8):1574–1581PubMedCrossRefGoogle Scholar
  42. 42.
    Laslett LJ, Alagona P, Clark BA, Drozda JP, Saldivar F, Wilson SR, Poe C, Hart M (2012) The worldwide environment of cardiovascular disease: prevalence, diagnosis, therapy, and policy issues: a report from the american college of cardiology. J Am Coll Cardiol 60(25_S):S1–S49Google Scholar
  43. 43.
    Latecki LJ, Megalooikonomou V, Wang Q, Lakämper R, Ratanamahatana C, Keogh EJ (2005) Partial elastic matching of time series. In: Fifth IEEE international conference on data mining ICDM, Houston TX, pp 701–704Google Scholar
  44. 44.
    Latecki LJ, Megalooikonomou V, Wang Q, Yu D (2007) An elastic partial shape matching technique. Pattern Recogn 40(11):3069–3080CrossRefGoogle Scholar
  45. 45.
    Latecki LJ, Wang Q, Koknar-Tezel S, Megalooikonomou V (2007) Optimal subsequence bijection. In: Seventh IEEE international conference on data mining, Omaha, pp 565–570Google Scholar
  46. 46.
    Lowe HC, Oesterle SN, Khachigian LM (2002) Coronary in-stent restenosis: current status and future strategies. J Am Coll Cardiol 39(2):183–193PubMedCrossRefGoogle Scholar
  47. 47.
    Markelj P, Tomaževič D, Likar B, Pernuš F (2012) A review of 3D/2D registration methods for image-guided interventions. Med Image Anal 16(3):642–661PubMedCrossRefGoogle Scholar
  48. 48.
    Mittal D, Kumar V, Saxena SC, Khandelwal N, Kalra N (2011) Neural network based focal liver lesion diagnosis using ultrasound images. Comput Med Imaging Graph 35(4):315–323PubMedCrossRefGoogle Scholar
  49. 49.
    Morales C, Radeva PC (2003) Vesselness enhancement diffusion. Pattern Recogn Lett 24(16):3141–3151CrossRefGoogle Scholar
  50. 50.
    Morlacchi S, Migliavacca F (2013) Modeling stented coronary arteries: where we are, where to go. Ann Biomed Eng 41(7):1428–1444PubMedCrossRefGoogle Scholar
  51. 51.
    Nanfeng S, Nigel W, Alun H, Simon TX, Yun X (2006) Fluid-wall modelling of mass transfer in an axisymmetric stenosis: effects of shear-dependent transport properties. Ann Biomed Eng 34(7):1119–1128CrossRefGoogle Scholar
  52. 52.
    Parodi O, Exarchos TP, Marraccini P, Vozzi F, Milosevic Z, Nikolic D, Sakellarios A, Siogkas PK, Fotiadis DI, Filipovic N (2012) Patient-specific prediction of coronary plaque growth from CTA angiography: a multiscale model for plaque formation and progression. IEEE Trans Inf Technol Biomed 16(5):952–965PubMedCrossRefGoogle Scholar
  53. 53.
    Piegl L, Tiller W (1995) The Nurbs book, 2nd edn. Springer, BerlinCrossRefGoogle Scholar
  54. 54.
    Robert M, Cothren S, Shekhar R, Murat TE, Steven E, Nissen J, Fredrick DC, Vince G (2000) Three-dimensional reconstruction of the coronary artery wall by image fusion of intravascular ultrasound and bi-plane angiography. Int J Cardiac Imaging 16(2):69–85CrossRefGoogle Scholar
  55. 55.
    Sakellarios A, Fotiadis D, Michalis L (2008) Finite element modeling of LDL transport in carotid artery bifurcations. EMBEC Conference; Antwerp, pp 1967–1971Google Scholar
  56. 56.
    Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process 26(1):43–49CrossRefGoogle Scholar
  57. 57.
    Scott NA (2006) Restenosis following implantation of bare metal coronary stents: pathophysiology and pathways involved in the vascular response to injury. Adv Drug Deliv Rev 58(3):358–376PubMedCrossRefGoogle Scholar
  58. 58.
    Shechter G, Devernay F, Coste-Maniere E, Quyyumi A, McVeigh ER (2003) Three-dimensional motion tracking of coro-nary arteries in biplane cineangiograms. IEEE Trans Med Imag 22(4):493–503CrossRefGoogle Scholar
  59. 59.
    Silverman ME (2006) Coronary-artery stents. N Engl J Med 354(6):483–495Google Scholar
  60. 60.
    Siogkas P, Sakellarios A, Exarchos TP, Athanasiou L, Karvounis E, Stefanou K, Fotiou E, Fotiadis DI, Naka KK, Michalis LK, Filipovic N, Parodi O (2011) Multiscale-patient-specic artery and atherogenesismodels. IEEE Trans Biomed Eng 58(12):3464–3468PubMedCrossRefGoogle Scholar
  61. 61.
    Stone PH, Coskun AU, Kinlay S, Clark PH, Sonka M, Wahle A, Ilegbusi OJ, Yeghiazarians Y, Popma JJ, Orav J, Kuntz RE, Feldman CL (2003) Effect of endothelial shear stress on the progression of coronary artery disease, vascular remodeling, and in-stent restenosis in humans: in vivo 6-month follow-up study. Circulation 108(4):438–444PubMedCrossRefGoogle Scholar
  62. 62.
    Takahashi T, Honda Y, Russo RJ, Fitzgerald PJ (2002) Intravascular ultrasound and quantitative coronary angiography 55(1):118–128Google Scholar
  63. 63.
    Taki A, Najafi Z, Roodaki A, Setarehdan SK, Zoroofi AR, König A, Navab N (2008) Automatic segmentation of calcified plaques and vessel borders in IVUS images. Int J Comput Assist Radiol Surg 3(3–4):347–354CrossRefGoogle Scholar
  64. 64.
    Toner D, Basir F, Lally C (2006) An investigation into the effect of stent strut thickness on restenosis using the finite element method and validation using an in vitro compliant artery model. J Biomech 39(1):S403CrossRefGoogle Scholar
  65. 65.
    Wahle A (2003) Coronary angiography and intravascular ultrasound—spatio-temporal modeling and quantification by data fusion. EFOMP 1:29–31Google Scholar
  66. 66.
    Wahle A, Oswald H, Fleck H (1996) 3D heart-vessel reconstruction from biplane angiograms. IEEE Comput Graph Appl 16(1):65–73CrossRefGoogle Scholar
  67. 67.
    Wahle A, Prause G, DeJong S, Sonka M (1999) Geometrically correct 3-D reconstruction of intravascular ultrasound images by fusion with biplane angiography—methods and validation. IEEE Trans Med Imaging 30(1):187–198Google Scholar
  68. 68.
    Weickert J (1998) Anisotropic diffusion in image processing. ECMI Series. Teubner-Verlag, StuttgartGoogle Scholar
  69. 69.
    Wijeysundera HC, Machado M, Farahati F, Wang X, Witteman W, van der Velde G, Tu JV, Lee DS, Goodman SG, Petrella R, O’Flaherty M, Krahn M, Capewell S (2010) Association of temporal trends in risk factors and treatment uptake with coronary heart disease mortality, 1994–2005. JAMA 303:1841–1847PubMedCrossRefGoogle Scholar
  70. 70.
    Xu C, Prince L (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369PubMedCrossRefGoogle Scholar
  71. 71.
    Yang J, Wang Y, Liu Y, Tang S, Chen W (2009) Novel approach for 3-D reconstruction of coronary arteries from two unca-librated angiographic images. IEEE Trans Image Process 18(7):1563–1572PubMedCrossRefGoogle Scholar
  72. 72.
    Zhang D, Lu G (2004) Review of shape representation and description techniques. Pattern Recogn 37(1):1–19CrossRefGoogle Scholar
  73. 73.
    Zhu H, Oakeson KD, Morton H (2003) Retrieval of cardiac phase from IVUS sequences. Med Imaging Ultrasonic Imaging Signal Process 5035:135–146Google Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2014

Authors and Affiliations

  • Arso M. Vukicevic
    • 1
  • Nemanja M. Stepanovic
    • 2
  • Gordana R. Jovicic
    • 1
  • Svetlana R. Apostolovic
    • 2
  • Nenad D. Filipovic
    • 1
  1. 1.Faculty of EngineeringUniversity of KragujevacKragujevacSerbia
  2. 2.Faculty of MedicineUniversity of NisNisSerbia

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