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Fusing in-vitro and in-vivo intravascular ultrasound data for plaque characterization


Accurate detection of in-vivo vulnerable plaque in coronary arteries is still an open problem. Recent studies show that it is highly related to tissue structure and composition. Intravascular Ultrasound (IVUS) is a powerful imaging technique that gives a detailed cross-sectional image of the vessel, allowing to explore arteries morphology. IVUS data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue. The main drawback of this method is the few number of available case studies and validated data due to the complex procedure of histological analysis of the tissue. On the other hand, IVUS data from in-vivo cases is easy to obtain but it can not be histologically validated. In this work, we propose to enhance the in-vitro training data set by selectively including examples from in-vivo plaques. For this purpose, a Sequential Floating Forward Selection method is reformulated in the context of plaque characterization. The enhanced classifier performance is validated on in-vitro data set, yielding an overall accuracy of 91.59% in discriminating among fibrotic, lipidic and calcified plaques, while reducing the gap between in-vivo and in-vitro data analysis. Experimental results suggest that the obtained classifier could be properly applied on in-vivo plaque characterization and also demonstrate that the common hypothesis of assuming the difference between in-vivo and in-vitro as negligible is incorrect.

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  1. 1.

    The use of the proposed technique on an in-vitro data set comprising the necrotic core tissue is a straightforward step that is expected not to change the overall behavior and performance of the proposed framework.

  2. 2.

    The function is described in pseudo-Matlab code.

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    The catheter is connected to the IVUS equipment by a motorized tool and its position is constantly monitored by X-ray analysis.


  1. 1.

    Shah PK (2003) Mechanism of plaque vulnerability and rupture. JACC 41(1):15–22

  2. 2.

    Burke AP, FarbA, Malcom GT, Liang Y, Smialek J, Virmani R (1997) Coronary risk factors and plaque morphology in men with coronary disease who died suddenly. New Engl J Med 336(18):1276–1281

  3. 3.

    Fuster V, Moreno PR, Fayad ZA, Corti R, Badimon JJ (2005) Atherothrombosis and high-risk plaque. JACC 46(6):937–954

  4. 4.

    Ehara S, Kobayashi Y, Yoshiyama M, Shimada K, Shimada Y, Fukuda D, Nakamura Y, Yamashita H, Yamagishi H, Takeuchi K, Naruko T, Haze K, Becker AE, Yoshikawa J, Ueda M (2004) Spotty calcification typifies the culprit plaque in patients with acute myocardial infarction: an intravascular ultrasound study. Circulation 110:3424–3429

  5. 5.

    Willerson JT, Wellens HJJ, Cohn JN, Holmes DR (2007) Atherosclerotic vulnerable plaques: pathophysiology, detection, and treatment. Cardiovas Med, Third Ed 10:621–639

  6. 6.

    Davies MJ, Thomas AC (1995) Plaque fissuring—the cause of acute myocardial infarction, sudden ischaemic death, and crescendo angina. Br Heart J 53(4):363–373

  7. 7.

    Zhang X, McKay CR, Sonka M (1998) Tissue characterization in intravascular ultrasound images. TMI 17(6):889–899

  8. 8.

    Caballero KL, Barajas J, Pujol O, Salvatella N, Radeva P (2006) In-vivo ivus tissue classification: a comparison between rf signal analysis and reconstructed image. Prog Pattern Recognit Image Anal Appl 4225/2006:137–146

  9. 9.

    Caballero KL, Barajas J, Pujol O, Rodriguez O, Radeva P (2007) Using reconstructed ivus images for coronary plaque classification. In: Proceedings of the 29th annual international conference of the IEEE EMBS

  10. 10.

    Moore M, Spencer T, Salter D, Kearney P, Shaw T, Starkey I, Fitzgerald P, Erbel R, Lange A, McDicken N, Sutherland G, Fox K (1998) Characterisation of coronary atherosclerotic morphology by spectral analysis of radiofrequency signal: in vitro intravascular ultrasound study with histological and radiological validation. Heart 79(5):459–467

  11. 11.

    Nair A, Kuban BD, Tuzcu EM, Schoenhagen P, Nissen SE, Vince DG (2002) Coronary plaque classification with intravascular ultrasound radiofrequency data analysis. Circulation 106:2200–2206

  12. 12.

    Nair A, Kuban BD, Obuchowski N, Vince GD (2001) Assessing spectral algorithms to predict atherosclerotic plaque composition with normalized and raw intravascular ultrasound data. UMB 27(10):1319–1331

  13. 13.

    Katouzian A, Sathyanarayana S, Baseri B, Konofagou EE, Carlier SG (2008) Challenges in atherosclerotic plaque characterization with intravascular ultrasound (ivus): from data collection to classification. TITB 12(3):315–27

  14. 14.

    Bedekar D (2003) Atherosclerotic plaque characterization by acoustic impedance analysis of intravascular ultrasound data. In: IEEE ultrasonic symposium, vol 1527

  15. 15.

    Sathyaranayana S, Carlier S, Wenguang L, Thomas L (2009) Characterization of atherosclerotic plaque by spectral similarity of radiofrequency intravascular ultrasound signals. EuroIntervention 5:133–139

  16. 16.

    Kawasaki M, Takatsu H, Noda T, Ito Y, Kunishima A, Arai M, Nishigaki K, Takemura G, Morita N, Minatoguchi S, Fujiwara H (2001) Noninvasive quantitative tissue characterization and two-dimensional color-coded map of human atherosclerotic lesions using ultrasound integrated backscatter. JACC 38(2):486–492

  17. 17.

    Kawasaki M, Takatsu H, Noda T, Sano K, Ito Y, Hayakawa K, Tsuchiya K, Arai M, Nishigaki K, Takemura G, Minatoguchi S, Fujiwara T, Fujiwara H (2002) Invivo quantitative tissue characterization of human coronary arterial plaques by use of integrated backscatter intravascular ultrasound and comparison with angioscopic findings. Circulation 105:2487–2492

  18. 18.

    Kawasaki M, Sano K, Okubo M, Yokoyama H, Ito Y, Murata I, Tsuchiya K, Minatoguchi S, Zhou X, Fujita H, Fujiwara H (2005) Volumetric quantitative analysis of tissue characteristics of coronary plaques after statin therapy using three-dimensional integrated backscatter intravascular ultrasound. JACC 45(12):1946–1953

  19. 19.

    Korte CD, Steen AD, Cespedes E, Pasterkamp G, Carlier SG, Mastik F, Schoneveld AH, Serruys PW, Bom N (2000) Characterization of plaque components and vulnerability with intravascular ultrasound elastography. Circulation 102:617–623

  20. 20.

    Korte CD, Sierevogel MJ, Mastik F, Strijder C, Schaar JA, Velema E, Pasterkamp G, Serruys PW, Steen AVD (2002) Identification of atherosclerotic plaque components with intravascular ultrasound elastography in vivo: a yucatan pig study. Circulation 105(14):1627–1630

  21. 21.

    Schaar JA, Regar E, Mastik F, McFadden EP, Saia F, Disco C, deKorte CL, Feyter PJ, der Steen AV, Serruys PW (2004) Characterizing vulnerable plaque features with intravascular elastography. Circulation 109:2716–2719

  22. 22.

    Murashige A, Hiro T, Fujii T, Imoto K, Murata T, Fukumoto Y, Matsuzaki M (2005) Detection of lipid-laden atherosclerotic plaque by wavelet analysis of radiofrequency intravascular ultrasound signals: in vitro validation and preliminary in vivo application. JACC 45(12):1954–1960

  23. 23.

    Pudil P, Ferri FJ, Novovicova J, Kittler J (1994) Floating search methods for feature selection with nonmonotonic criterion functions. In: Proceedings of the 12th IAPR international conference on pattern recognition, vol 2, pp 279–283

  24. 24.

    Ciompi F (2008) Ecoc-based plaque classification using in-vivo and ex-vivo intravascular ultrasound data. Master thesis

  25. 25.

    Dietterich TG, Bakiri G (1995) Solving multiclass learning problems via error-correcting output codes. J Artif Intell Res 2:263–286

  26. 26.

    Allwein EL, Schapire RE, Singer Y (2000) Reducing multiclass to binary: a unifying approach for margin classifiers. In: Proceedings of the seventeenth international conference on machine learning, pp 9–16

  27. 27.

    Schapire R (2001) The boosting approach to machine learning: an overview. MSRI workshop on nonlinear estimation and classification

  28. 28.

    Gonzalez RC, Woods RE (2001) Digital image processing, 2nd edn. Prentice Hall, Upper Saddle River

  29. 29.

    Bovik AC, Clark M, Geisler WS (1990) Multichannel texture analysis using localized spatial filters. TPAMI 12(1):55–73

  30. 30.

    Ojala T, Pietikäien M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. TPAMI 24(7):971–987

  31. 31.

    Pujol O, Vitria J, Radeva P (2006) Discriminant ecoc: a heuristic method for application dependent design of error correcting output codes. TPAMI 28(6):1007–1012

  32. 32.

    Escalera S, Pujol O, Mauri J, Radeva P (2008) Ivus tissue characterization with sub-class error-correcting output codes. J Signal Process Syst 55:35–47

  33. 33.

    Rifkin R, Klautau A (2004) In defense of one-vs-all classification. J Mach Learn Res 5:101–141

  34. 34.

    Djouadi A, Snorrason O, Garber FD (1990) The quality of training sample estimates of the bhattacharyya coefficient. PAMI 12(1):92–97. doi:10.1109/34.41388

  35. 35.

    Borg I, Groenen P (2005) Modern multidimensional scaling: theory and applications, 2nd edn. Springer, New York

  36. 36.

    Chapelle O, Zien A (2004) Semi-supervised classification by low density separation. In: Proceeding of the tenth internationl workshop on artificial intelligence and statistics, pp 57–64

  37. 37.

    Wang J, Shen Z, Pan W (2007) On transductive support vector machines. Predict Discov

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This work was supported in part by a research grant from projects TIN2006-15308-C02, TIN2009-14404-C02, FIS-PI061290, FIS-PI060957, FIS-PI070454, CONSOLIDER INGENIO 2010 (CSD2007-00018).

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Correspondence to Francesco Ciompi.

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Ciompi, F., Pujol, O., Gatta, C. et al. Fusing in-vitro and in-vivo intravascular ultrasound data for plaque characterization. Int J Cardiovasc Imaging 26, 763–779 (2010).

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  • Intravascular ultrasound
  • Plaque characterization
  • Radio frequency analysis
  • Sequential floating forward selection