Advertisement

Methods in Atherosclerotic Plaque Characterization Using Intravascular Ultrasound Images and Backscattered Signals

  • Amin Katouzian
  • Stéphane G. Carlier
  • Andrew F. LaineEmail author
Chapter

Abstract

We will review existing supervised as well as unsupervised image- and spectrumderived algorithms in the context of atherosclerotic plaque characterization and detection of vulnerable plaques. We will further elaborate more on challenges involved in characterization of plaques from tissue preparation, data collection, and registration toward classification.

Keywords

Atherosclerosis Plaque characterization IVUS 

References

  1. 1.
    M. Naghavi, P. Libby, E. Falk, et al., “From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies – Part I,” Circulation, 108, 1664–1672 (2003).CrossRefPubMedGoogle Scholar
  2. 2.
    J. Mackay, G. Mensah, World Health Organization and US Centers for Disease Control and Prevention, “The atlas of heart disease and stroke,” Geneva: WHO (2004).Google Scholar
  3. 3.
    J. A. Ambrose, M. A. Tannenbaum, D. Alexopoulos, et al., “Angiographic progression of coronary artery disease and the development of myocardial infarction,” J. Am. Coll. Cardiol., 12(1), 56–62 (1988).CrossRefPubMedGoogle Scholar
  4. 4.
    D. Hackett, G. Davies, A. Maseri, “Pre-existing coronary stenoses in patients with first myocardial infarction are not necessarily severe,” J. Eur. Heart, 9(12), 1317–23 (1988).Google Scholar
  5. 5.
    D. Giroud, J. M. Li, P. Urban, et al., “Relation of the site of acute myocardial infarction to the most severe coronary arterial stenosis at prior angiography,” J. Am. Cardiol., 69(8), 729–732 (1992).CrossRefGoogle Scholar
  6. 6.
    P. D. Richardson, M. J. Davies, G. V. R. Born, “Influence of plaque configuration and stress distribution on fissuring of coronary atherosclerotic plaques,” Lancet, 21, 941–944 (1989).CrossRefGoogle Scholar
  7. 7.
    J. H. Qiao, M. C. Fishbein, “The severity of coronary atherosclerosis at sites of plaque rupture with occlusive thrombosis,” J. Am. Coll. Cardiol., 17(5), 1138–1142 (1991).CrossRefPubMedGoogle Scholar
  8. 8.
    A. P. Burke, A. Farb, G. T. Malcom, et al., “Coronary risk factors and plaque morphology in men with coronary disease who died suddenly,” N. Engl. J. Med., 336(18), 1276–1282 (1997).CrossRefPubMedGoogle Scholar
  9. 9.
    R. Virmani, F. D. Kolodgie, A. P. Burke, et al., “Lessons from sudden coronary death: a comprehensive morphological classification scheme for atherosclerotic lesions,” Arterioscler. Thromb. Vasc. Biol., 20(5), 1262–1275 (2000).PubMedGoogle Scholar
  10. 10.
    E. Falk, “Pathogenesis of atherosclerosis,” J. Am. Coll. Cardiol., 47(8 Suppl), C7–C12 (2006).CrossRefPubMedGoogle Scholar
  11. 11.
    P. Schoenhagen, K. M. Ziada, S. R. Kapadia, et al., “Extent and direction of arterial remodeling in stable versus unstable coronary syndromes: an intravascular ultrasound study,” Circulation, 101(6), 598–603 (2000).PubMedGoogle Scholar
  12. 12.
    A. Maehara, G. S. Mintz, A. B. Bui, et al., “Morphologic and angiographic features of coronary plaque rupture detected by intravascular ultrasound,” J. Am. Coll. Cardiol., 40(5), 904–910 (2002).CrossRefPubMedGoogle Scholar
  13. 13.
    K. Fujii, G. S. Mintz, S. G. Carlier, et al., “Intravascular ultrasound profile analysis of ruptured coronary plaques,” Am. J. Cardiol., 98(4), 429–435 (2006).CrossRefPubMedGoogle Scholar
  14. 14.
    J. E. Muller, A. Tawakol, S. Kathiresan, J. Narula, “New opportunities for identification and reduction of coronary risk: treatment of vulnerable patients, arteries, and plaques,” J. Am. Coll. Cardiol., 47, C2–C6 (2006).CrossRefPubMedGoogle Scholar
  15. 15.
    W. C. Little, “Angiographic assessment of the culprit coronary artery lesion before acute myocardial infarction,” Am. J. Cardiol., 66, 44G–47G (1990).CrossRefPubMedGoogle Scholar
  16. 16.
    J. E. Muller, G. H. Tofler, “Triggering and hourly variation of onset of arterial thrombosis,” Ann. Epidemiol., 2, 393–405 (1992).CrossRefPubMedGoogle Scholar
  17. 17.
    E. Falk, “Why do plaques rupture?” Circulation, 86, III30–III42 (1992).PubMedGoogle Scholar
  18. 18.
    P. Libby, “Molecular bases of the acute coronary syndromes,” Circulation, 91, 2844–2850 (1995).PubMedGoogle Scholar
  19. 19.
    R. Virmani, A. P. Burke, F. D. Kolodgie, A. Farb, “Pathology of the thin-cap fibroatheroma: a type of vulnerable plaque,” J. Inteven. Cardiol., 16(3), 267–272 (2003).CrossRefGoogle Scholar
  20. 20.
    A. Katouzian, S. Sathyanarayana, B. Baseri, E. E. Konofagou, S. G. Carlier, “Challenges in atherosclerotic plaque characterization with intravascular ultrasound (IVUS): from data collection to classification,” IEEE Trans. Inf. Technol. Biomed., 12(3), 315–327 (2008).CrossRefPubMedGoogle Scholar
  21. 21.
    J. F. Granada, D. W. Bradley, H. K. Win, C. L. Alviar, A. Builes, E. I. Lev, R. Barrios, D. G. Schulz, A. E. Raizner, G. L. Kaluza, “In vivo plaque characterization using intravascular ultrasound-virtual histology in a porcine model of complex coronary lesions,” Arterioscler. Thromb. Vasc. Biol., 27(2), 387–393 (2007).CrossRefPubMedGoogle Scholar
  22. 22.
    A. Katouzian, S. Sathyanarayana, W. Li, T. Thomas, S. G. Carlier, “Challenges in tissue ­characterization from backscattered intravascular ultrasound signals,” Proceeding of SPIE (2006).Google Scholar
  23. 23.
    L. Fellingham, F. Sommer, “Ultrasonic characterization of tissue structure in the in vivo human liver and spleen,” IEEE Trans. Sonics Ultrason., SU-31(4), 418–428 (1984).Google Scholar
  24. 24.
    L. Landini, L. Verrazzani, “Spectral characterization of tissue microstructure by ultrasound:Astochastic approach,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 37(5), 448–456 (1990).CrossRefPubMedGoogle Scholar
  25. 25.
    W. C. A. Pereira, S. L. Bridal, A. Coron, P. Laugier, “Singular spectrum analysis applied to backscattered ultrasound signals from in vitro human cancellous bone specimens,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 51(3), 302–312 (2004).CrossRefPubMedGoogle Scholar
  26. 26.
    J. Y. David, S. A. Jones, D. P. Giddens, “Modern spectral analysis techniques for blood flow velocity and spectral measurements with pulsed doppler ultrasound,” IEEE Trans. Biomed. Eng., 38(6), 589–596 (1991).CrossRefPubMedGoogle Scholar
  27. 27.
    F. L. Lizzi, M. Ostromogilsky, E. J. Feleppa, M. C. Rorke, and M. M. Yaremko, “Relationship of ultrasonic spectral parameters to features of tissue microstructure,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, UFFC-34(3), 319–329 (1986).Google Scholar
  28. 28.
    F. Ossant, M. Lebertre, L. Pourcelot, F. Patat, “Ultrasonic characterization of maturation of fetal lung microstructure: an animal study,” Ultrasound Med. Biol., 27(2), 157–169 (2001).CrossRefPubMedGoogle Scholar
  29. 29.
    F. L. Lizzi, M. A. Laviola, and D. J. Coleman, “Tissue signature characterization utilizing frequency domain analysis,” in Proc. IEEE Ultrason. Symp., 714–719 (1976).Google Scholar
  30. 30.
    A. Nair, B. D. Kuban, N. Obuchowski, D. G. Vince, “Assessing spectral algorithms to predict atherosclerotic plaque composition with normalized and raw intravascular ultrasound data,” Ultrasound Med. Biol., 27(10), 1319–1331 (2001).CrossRefPubMedGoogle Scholar
  31. 31.
    F. L. Lizzi, M. Greenebaum, E. J. Feleppa, M. Elbaum, D. J. Coleman, “Theoretical ­framework for spectrum analysis in ultrasonic tissue characterization,” J. Acoust. Soc. Am., 73(4), 1366–1373 (1983).CrossRefPubMedGoogle Scholar
  32. 32.
    A. Nair, D. G. Vince, D. Calvetti, “Blind data calibration of intravascular ultrasound data for automated tissue characterization,” Proc. IEEE Ultrason. Symp., 2, 1126–1129 (2004).Google Scholar
  33. 33.
    A. Nair, B. D. Kuban, M. Tuzcu, P. Schoenhagen, S. E. Nissen, D. G. Vince, “Coronary plaque classification with intravascular ultrasound radiofrequency data analysis,” Circulation, 106(17), 2200–2206 (2002).CrossRefPubMedGoogle Scholar
  34. 34.
    A. Nair, D. Calvetti, D. G. Vince, “Regularized autoregressive analysis of intravascular ultrasound backscatter: improvement in spatial accuracy of tissue maps,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 51(4), 420–431 (2004).CrossRefGoogle Scholar
  35. 35.
    M. Kawasaki, H. Takatsu, T. Noda, et al., “Noninvasive quantitative tissue characterization and two-dimensional color-coded map of human atherosclerotic lesions using ultrasound integrated backscatter: comparison between histology and integrated backscatter images,” J. Am. Coll. Cardiol., 38(2), 486–92 (2002).CrossRefGoogle Scholar
  36. 36.
    M. Kawasaki, H. Takatsu, T. Noda, et al., “In vivo quantitative tissue characterization of human coronary arterial plaques by use of integrated backscatter intravascular ultrasound and comparison with angioscopic findings,” Circulation,105(21), 2487–2492 (2002).CrossRefPubMedGoogle Scholar
  37. 37.
    M. Okubo, M. Kawasaki, Y. Ishihara, et al., “Tissue characterization of coronary plaques: comparison of integrated backscatter intravascular ultrasound with virtual histology intravascular ultrasound,” Circulation Jpn., 72, 1631–1639 (2008).CrossRefGoogle Scholar
  38. 38.
    L. K. Ryan, F. S. Foster, “Ultrasonic measurement of differential displacement and strain in vascular model,” Ultrason. Imaging, 19, 19–38 (1997).PubMedGoogle Scholar
  39. 39.
    H. E. Talhmai, L. S. Wilson, M. L. Neale, “Spectral tissue strain: a new technique for imaging tissue strain using intravascular ultrasound,” Ultrasound Med. Biol., 20, 759–772 (1994).CrossRefGoogle Scholar
  40. 40.
    B. M. Shapo, J. R. Crowe, A. R. Skovoroda, M. Eberle, N. A. Cohn, M. O’Donnell, “Displacement and strain imaging of coronary arteries with intraluminal ultrasound,” IEEE Trans. UFFC, 43, 234–246 (1996).Google Scholar
  41. 41.
    C. L. de Korte, A. F. W. van der Steen, E. I. Céspedes, G. Pasterkamp, S. G. Carlier, F. Mastik, A. H. Schoneveld, P. W. Serruys, N. Bom, “Characterization of plaque components and vulnerability with intravascular ultrasound elastography,” Physics Med. Biol., 45, 1465–1475 (2000).CrossRefGoogle Scholar
  42. 42.
    R. L. Maurice, J. Fromagea, M. R. Cardinal, M. Doyley, E. de Muinck, J. Robb, G. Cloutier, “Characterization of atherosclerotic plaques and mural thrombi with intravascular ultrasound elastography: a potetial method evaluated in an aortic rabbit model and a human coronary artery,” IEEE Trans. Inf. Technol. Biomed., 12(3), 290–298 (2008).CrossRefPubMedGoogle Scholar
  43. 43.
    J. M. Tobis, J. Mallery, D. Mahon, K. Lehmann, P. Zalesky, J. Griffith, J. Gessert, M. Moriuchi, M. McRae, M. L. Dwyer, “Intravascular ultrasound imaging of human coronary arteries in vivo. Analysis of tissue characterization with comparison to in vitro histological specimens,” Circulation, 83, 913–926 (1991).PubMedGoogle Scholar
  44. 44.
    K. Gad, M. B. Leon, “Characterization of atherosclerotic lesions by intravascular ultrasound: possible role in unstable coronary syndromes and in interventional theraputics procedure,” Am. J. Cardiol., 68, 85B–91B (1991).CrossRefGoogle Scholar
  45. 45.
    X. Zhang, C. R. McKay, M. Sonka, “Tissue characterization in intravascular ultrasound images,” IEEE Trans. Med. Imaging, 17(6), 889–899 (1998).CrossRefPubMedGoogle Scholar
  46. 46.
    K. L. Caballero, O. Pujol, J. Barajas, J. Mauri, P. Radeva, “Assessing in-vivo IVUS tissue classification accuracy between normalized image reconstruction and RF analysis,” MICCAI, 82–89 (2006).Google Scholar
  47. 47.
    E. Brunenberg, O. Pujol, B. H. Romeny, P. Radeva, “Automatic IVUS segmentation of atherosclerotic plaque with stop & go snake,” Med. Image Comput. Comput. Assist. Interv., 9(2), 9–16 (2006).PubMedGoogle Scholar
  48. 48.
    R. De Valois, K. De Valois, “Spatial vision,” New York: Oxford University Press (1988).Google Scholar
  49. 49.
    J. Beck, A. Sutter, R. Ivry, “Spatial frequency channels and perceptual grouping in texture segregation,” Comput. Vis. Graph. Image Process., 37, 299–325 (1987).CrossRefGoogle Scholar
  50. 50.
    A. Katouzian, B. Baseri, E. E. Konofagou, A. F. Laine, “Texture-driven coronary artery plaque characterization using wavelet packet signatures,” IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) (2008).Google Scholar
  51. 51.
    S. G. Mallat, “A theory of multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell., 11(7), 674–693 (1989).CrossRefGoogle Scholar
  52. 52.
    T. Hiro, C. Y. Leung, S. De Guzman, V. J. Caiozzo, A. R. Fervid, H. Karimi, R. H. Helfant, J. M. Tobis, “Are soft echoes really soft? Intravascular ultrasound assessment of mechanical properties in human atherosclerotic tissue,” Am. Heart J., 133, 1–7 (1997).CrossRefPubMedGoogle Scholar
  53. 53.
    A. Jeremias, M. L. Kolz, T. S. Ikonen, J. F. Gummert, A. Oshima, M. Hayase, Y. Honda, N. Komiyama, G. J. Berry, R. E. Morris, P. G. Yock, P. J. Fitzgerald, “Feasibility of in vivo intravascular ultrasound tissue characterization in the detection of early vascular transplant rejection,” Circulation, 100, 2127–2130 (1999).PubMedGoogle Scholar
  54. 54.
    S. Escalera, O. Pujol, J. Mauri, P. Radeva, “Intravascular ultrasound tissue characterization with sub-class error-correcting output codes,” J. Sign. Process. Syst., 55(1–3), 35–47 (2009).CrossRefGoogle Scholar
  55. 55.
    P. Ohanian, R. Dubes, “Performance evaluation for four classes of textural features,” Pattern Recognit., 25, 819–833 (1992).CrossRefGoogle Scholar
  56. 56.
    T. Ojala, M. Pietikainen, T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., 24, 971–987 (2002).CrossRefGoogle Scholar
  57. 57.
    J. G. Daugman, “Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters,” J. Opt. Soc. Am., 2(A), 1160–1169 (1985).CrossRefGoogle Scholar
  58. 58.
    A. C. Bovik, M. Clark, W. S. Geisler, “Multichannel texture analysis using localized spatial filters,” IEEE Trans. Pattern Anal. Mach. Intell., 12(1), 55–73 (1990).CrossRefGoogle Scholar
  59. 59.
    T. Hastie, R. Tibshirani, “Classification by pairwise grouping,” NIPS, 26, 451–471 (1998).Google Scholar
  60. 60.
    N. J. Nilsson, “Learning machines,” New York: McGraw-Hill (1965).Google Scholar
  61. 61.
    E. Allwein, R. Schapire, Y. Singer, “Reducing multiclass to binary: a unifying approach for margin classifiers,” J. Mach. Learn. Res., 1, 113–141 (2002).CrossRefGoogle Scholar
  62. 62.
    O. Pujol, P. Radeva, J. Vitrià, “Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes,” IEEE Trans. Pattern Anal. Mach. Intell., 28, 1007–1012 (2006).CrossRefPubMedGoogle Scholar
  63. 63.
    J. Friedman, T. Hastie, R. Tibshirani, “Additive logistic regression: a statistical view of boosting,” Ann. Stat., 38, 337–374 (1998).Google Scholar
  64. 64.
    A. Taki, A. Roodaki, O. Pauly, S. Setarehdan, G. Unal, N. Navab, “A new method for characterization of coronary plaque composition via ivus images,” IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) (2009).Google Scholar
  65. 65.
    N. Bruining, S. Verheye, M. Knaapen, “Three-dimensional and quantitative analysis of ­atherosclerotic plaque composition by automated differential echogenicity,” Catheter. Cardiovasc. Interv., 70(7), 968–978 (2007).CrossRefPubMedGoogle Scholar
  66. 66.
    G. S. Mintz, S. E. Nissen, W. D. Anderson, “American college of cardiology clinical expert consensus document on standards for acquisition, measurement and reporting of intravascular ultrasound studies (IVUS): a report of the American college of cardiology task force on clinical expert consensus documents,” J. Am. Coll. Cardiol., 37, 1478–1492 (2001).CrossRefPubMedGoogle Scholar
  67. 67.
    K. Tanaka, S. Carlier, A. Katouzian, G. Mintz, “Characterization of the intravascular ultrasound radiofrequency signal within regions of acoustic shadowing behind calcium,” J. Am. Coll. Cardiol., 49(9 Suppl B), 29B (2007).Google Scholar
  68. 68.
    T. Ojala, M. Pietikäinen, T. Mäenpää, “Multiresolution gray-scale and rotation invariant ­texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., 24(7), 971–987 (2002).CrossRefGoogle Scholar
  69. 69.
    X. Tang, “Texture information in run length matrices,” IEEE Trans. Image Process., 7(11), 1602–1609 (1998).CrossRefPubMedGoogle Scholar
  70. 70.
    E. Picano, L. Landdini, A. Distante, et al., “Angle dependence of ultrasonic backscatter in arterial tissues: a study in vitro,” Circulation, 72, 572–576 (1985).PubMedGoogle Scholar
  71. 71.
    G. S. Mintz, E. Missel, “What is behind the calcium? The relationship between calcium and necrotic core on virtual histology analyses: reply,” Letters to the Editor, European Heart Journal, 30, 125–126 (2009).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Amin Katouzian
  • Stéphane G. Carlier
  • Andrew F. Laine
    • 1
    Email author
  1. 1.Biomedical Engineering DepartmentColumbia UniversityNew YorkUSA

Personalised recommendations