Advertisement

Systematic mapping study on diagnosis of vulnerable plaque

  • Zahra RezaeiEmail author
  • Ali Selamat
  • Arash Taki
  • Mohd Shafry Mohd Rahim
  • Mohammed Rafiq Abdul Kadir
Article
  • 35 Downloads

Abstract

Post-mortem studies demonstrate that around two thirds of all myocardial infarctions are typically result of the plaque rupture. In this paper, systematic mapping study is applied to specify the vulnerable plaque research area. The scope of this research has been limited to the published papers of IEEE Transactions, Sciencedirect, and Springer between 2000 and 2016 years. The related studies are categorized into research question, research strategy, research challenge, and research framework. Based on the mapping results, the researchers are focused on the clinical analysis and algorithmic approach. This paper describes a review of state-of-the-art literature on TCFA detection techniques, motivations, issues, and existing challenges in terms of imaging modalities, plaque characterization techniques, and plaque type classification. A summary of each study containing the author names, publication year, technique, advantages, and drawbacks is presented at the end of each subsection.

Keywords

Systematic mapping TCFA Plaque characterization IVUS segmentation 

Notes

References

  1. 1.
    Acharya UR et al (2015) Ultrasound-based tissue characterization and classification of fatty liver disease: A screening and diagnostic paradigm. Knowl-Based Syst 75:66–77Google Scholar
  2. 2.
    Anooj P (2012) Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules. Journal of King Saud University-Computer and Information Sciences 24(1):27–40Google Scholar
  3. 3.
    Athanasiou LS et al (2012) A Novel Semiautomated Atherosclerotic Plaque Characterization Method Using Grayscale Intravascular Ultrasound Images: Comparison With Virtual Histology. Information Technology in Biomedicine, IEEE Transactions 16(3):391–400Google Scholar
  4. 4.
    Athanasiou LS et al (2013) A hybrid plaque characterization method using intravascular ultrasound images. Technol Health Care 21(3):199–216Google Scholar
  5. 5.
    Athanasiou L et al (2014) Currently available methodologies for the processing of intravascular ultrasound and optical coherence tomography images. Expert Rev Cardiovasc Ther 12(7):885–900Google Scholar
  6. 6.
    Athanasiou LS et al (2015) Error propagation in the characterization of atheromatic plaque types based on imaging. Comput Methods Prog Biomed 121(3):161–174Google Scholar
  7. 7.
    Balocco S et al (2014) Standardized evaluation methodology and reference database for evaluating IVUS image segmentation. Comput Med Imaging Graph 38(2):70–90Google Scholar
  8. 8.
    Batty JA et al (2016) Intracoronary imaging in the detection of vulnerable plaques. Curr Cardiol Rep 18(3):28Google Scholar
  9. 9.
    Bourantas CV et al (2013) Hybrid intravascular imaging: current applications and prospective potential in the study of coronary atherosclerosis. J Am Coll Cardiol 61(13):1369–1378Google Scholar
  10. 10.
    Bourantas CV et al (2016) Vulnerable plaque detection: an unrealistic quest or a feasible objective with a clinical value? Heart 102(8):581–589Google Scholar
  11. 11.
    Brown AJ et al (2015) Direct comparison of virtual-histology intravascular ultrasound and optical coherence tomography imaging for identification of thin-cap fibroatheroma. Circulation: Cardiovascular Imaging 8(10):e003487Google Scholar
  12. 12.
    Brugaletta S et al (2016) Stable coronary artery disease. Is it really stable? Lesion morphology interpretation by Grayscale and VH-IVUS in patients with coronary artery disease. Continuing Cardiology Education 2(2):66–76Google Scholar
  13. 13.
    Chan LW, Sun Y, Benzie IF (2013) Discrimination and stratification tests of cardiovascular disease risk assessment models against ultrasound detection of carotid plaques in type 2 diabetics. Health 5(7A1):1–10Google Scholar
  14. 14.
    Cilla M (2013) Mechanical effects on the atheroma plaque appearance, growth and vulnerabilityGoogle Scholar
  15. 15.
    Ciompi F et al (2012) HoliMAb: A holistic approach for Media–Adventitia border detection in intravascular ultrasound. Med Image Anal 16(6):1085–1100Google Scholar
  16. 16.
    Corban MT et al (2014) Combination of plaque burden, wall shear stress, and plaque phenotype has incremental value for prediction of coronary atherosclerotic plaque progression and vulnerability. Atherosclerosis 232(2):271–276Google Scholar
  17. 17.
    Czopek K, Legutko J, Jąkała J (2011) Quantitative assessment for confluent plaque area related to diagnostic IVUS/VH images. in Computing in Cardiology, 2011. IEEEGoogle Scholar
  18. 18.
    Czopek K, Legutko J, Jąkała J Quantitative assessment for confluent plaque area related to diagnostic IVUS/VH images. Comput Cardiol 2011(38):717–720Google Scholar
  19. 19.
    de Graaf MA et al (2013) Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology. The International Journal of Cardiovascular Imaging 29(5):1177–1190Google Scholar
  20. 20.
    Dehnavi SM et al (2013) Automatic soft and hard plaque detection in IVUS images: A textural approach. In: IEEE Conference on Information & Communication Technologies (ICT). IEEEGoogle Scholar
  21. 21.
    Dehnavi SM et al (2013) Automatic soft and hard plaque detection in IVUS images: A textural approach. In: Information & Communication Technologies (ICT), 2013 IEEE Conference on. IEEEGoogle Scholar
  22. 22.
    Downe RW (2013) Predictive analysis of coronary plaque morphology and composition on a one year timescale. University of Iowa, Iowa CityGoogle Scholar
  23. 23.
    Escalera S et al (2009) Intravascular ultrasound tissue characterization with sub-class error-correcting output codes. Journal of Signal Processing Systems 55(1–3):35–47Google Scholar
  24. 24.
    Essa E et al (2011) Automatic IVUS media-adventitia border extraction using double interface graph cut segmentation. in 18th IEEE International Conference on Image Processing. IEEEGoogle Scholar
  25. 25.
    Filho ES et al (2008) Detection and quantification of calcifications in intravascular ultrasound images by automatic thresholding. Ultrasound Med Biol 34(1):160–165Google Scholar
  26. 26.
    Finn AV et al (2010) Concept of vulnerable/unstable plaque. Arterioscler Thromb Vasc Biol 30(7):1282–1292Google Scholar
  27. 27.
    Fleg JL et al (2012) Detection of High-Risk Atherosclerotic PlaqueReport of the NHLBI Working Group on Current Status and Future Directions. JACC Cardiovasc Imaging 5(9):941–955Google Scholar
  28. 28.
    Foster B et al (2014) A review on segmentation of positron emission tomography images. Comput Biol Med 50:76–96Google Scholar
  29. 29.
    Fujii K et al (2015) Accuracy of OCT, Grayscale IVUS, and Their Combination for the Diagnosis of Coronary TCFA: An Ex Vivo Validation Study. JACC Cardiovasc Imaging 8(4):451–460Google Scholar
  30. 30.
    Garcia-Garcia HM, Costa MA, Serruys PW (2010) Imaging of coronary atherosclerosis: intravascular ultrasound. Eur Heart J 31(20):2456–2469Google Scholar
  31. 31.
    Giannoglou VG, Stavrakoudis DG, Theocharis JB (2012) IVUS-based characterization of atherosclerotic plaques using feature selection and SVM classification. in 12th International Conference on Bioinformatics & Bioengineering (BIBE). IEEEGoogle Scholar
  32. 32.
    Giannoglou V, Theocharis J (2014) Decision Fusion of Multiple Classifiers for Coronary Plaque Characterization from IVUS Images. International Journal on Artificial Intelligence Tools 23(03):1460005Google Scholar
  33. 33.
    Giannoglou GD et al (2007) A novel active contour model for fully automated segmentation of intravascular ultrasound images: in vivo validation in human coronary arteries. Comput Biol Med 37(9):1292–1302Google Scholar
  34. 34.
    Giannoglou VG et al (2012) Genetic fuzzy rule-based classification systems for tissue characterization of intravascular ultrasound images. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEEGoogle Scholar
  35. 35.
    Giannoglou VG et al (2015) Genetic fuzzy rule based classification systems for coronary plaque characterization based on intravascular ultrasound images. Eng Appl Artif Intell 38:203–220Google Scholar
  36. 36.
    Gogas BD et al (2011) Assessment of coronary atherosclerosis by IVUS and IVUS-based imaging modalities: progression and regression studies, tissue composition and beyond. The International Journal of Cardiovascular Imaging 27(2):225–237Google Scholar
  37. 37.
    Honda S et al (2016) Characterization of coronary atherosclerosis by intravascular imaging modalities. Cardiovascular Diagnosis and Therapy 6(4):368–381Google Scholar
  38. 38.
    Hong YJ et al (2010) Plaque components at coronary sites with focal spasm in patients with variant angina: Virtual histology-intravascular ultrasound analysis. Int J Cardiol 144(3):367–372Google Scholar
  39. 39.
    Hong YJ et al (2011) Impact of plaque components on no-reflow phenomenon after stent deployment in patients with acute coronary syndrome: a virtual histology-intravascular ultrasound analysis. Eur Heart J 32(16):2059–2066Google Scholar
  40. 40.
    Jodas DS, Pereira AS, Tavares JMR (2016) A review of computational methods applied for identification and quantification of atherosclerotic plaques in images. Expert Syst Appl 46:1–14Google Scholar
  41. 41.
    Jones J-L et al (2013) Interactive segmentation of media-adventitia border in ivus. In: Computer Analysis of Images and Patterns. SpringerGoogle Scholar
  42. 42.
    Karamalis A et al (2012) Ultrasound confidence maps using random walks. Med Image Anal 16(6):1101–1112Google Scholar
  43. 43.
    Katouzian A (2011) Quantifying Atherosclerosis: IVUS Imaging For Lumen Border Detection And Plaque Characterization. Columbia Univ, New YorkGoogle Scholar
  44. 44.
    Katouzian A, et al (2008) Texture-driven coronary artery plaque characterization using wavelet packet signatures. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE.Google Scholar
  45. 45.
    Katouzian A et al (2010) Automatic detection of luminal borders in IVUS images by magnitude-phase histograms of complex brushlet coefficients. in 32nd Annual International Conference of the IEEE EMBS. Buenos Aires: IEEEGoogle Scholar
  46. 46.
    Katouzian A et al (2012) A state-of-the-art review on segmentation algorithms in intravascular ultrasound (IVUS) images. IEEE Trans Inf Technol Biomed 16(5):823–834Google Scholar
  47. 47.
    Katouzian A et al (2012) Iterative self-organizing atherosclerotic tissue labeling in intravascular ultrasound images and comparison with virtual histology. IEEE Trans Biomed Eng 59(11):3039–3049Google Scholar
  48. 48.
    König A, Klauss V (2007) Virtual histology. Heart 93(8):977–982Google Scholar
  49. 49.
    König A et al (2008) Technology insight: in vivo coronary plaque classification by intravascular ultrasonography radiofrequency analysis. Nature Clinical Practice Cardiovascular Medicine 5(4):219–229Google Scholar
  50. 50.
    Konig A et al (2010) Intravascular ultrasound radiofrequency analysis of the lesion segment profile in ACS patients. Clin Res Cardiol 99(2):83–91Google Scholar
  51. 51.
    Kubo T et al (2010) The dynamic nature of coronary artery lesion morphology assessed by serial virtual histology intravascular ultrasound tissue characterization. J Am Coll Cardiol 55(15):1590–1597Google Scholar
  52. 52.
    Kubo T et al (2011) Virtual histology intravascular ultrasound compared with optical coherence tomography for identification of thin-cap fibroatheroma. Int Heart J 52(3):175–179Google Scholar
  53. 53.
    Lazrag H, Aloui K, Naceur MS (2013) Automatic segmentation of lumen in intravascular ultrasound images using fuzzy clustering and active contours. Proceedings Engineering & Technology (1):58–63Google Scholar
  54. 54.
    Liang M, Puri A, Devlin G (2011) The vulnerable plaque: the real villain in acute coronary syndromes. The Open Cardiovascular Medicine Journal 5:123Google Scholar
  55. 55.
    Madssen E et al (2014) Coronary atheroma regression and plaque characteristics assessed by grayscale and radiofrequency intravascular ultrasound after aerobic exercise. Am J Cardiol 114(10):1504–1511Google Scholar
  56. 56.
    Maehara A et al (2012) Definitions and methodology for the grayscale and radiofrequency intravascular ultrasound and coronary angiographic analyses. JACC Cardiovasc Imaging 5(3s1):S1–S9Google Scholar
  57. 57.
    Margolis MP et al (2009) Automated lesion analysis based upon automatic plaque characterization according to a classification criterion, Google PatentsGoogle Scholar
  58. 58.
    McDaniel MC et al (2011) Contemporary clinical applications of coronary intravascular ultrasound. J Am Coll Cardiol Intv 4(11):1155–1167Google Scholar
  59. 59.
    Mendizabal-Ruiz EG, Rivera M, Kakadiaris IA (2013) Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach. Med Image Anal 17(6):649–670Google Scholar
  60. 60.
    Mesejoa P et al (2016) A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis. Appl Soft Comput 44:1–29Google Scholar
  61. 61.
    Mishra T, Mishra C, Das B (2013) An approach to the classification, diagnosis and management of vulnerable plaque. Journal of Indian College of CardiologyGoogle Scholar
  62. 62.
    Naghavi M et al (2006) From vulnerable plaque to vulnerable patient--Part III: Executive summary of the Screening for Heart Attack Prevention and Education (SHAPE) Task Force report. Am J Cardiol 98(2A):2HGoogle Scholar
  63. 63.
    Nair A et al (2001) Assessing spectral algorithms to predict atherosclerotic plaque composition with normalized and raw intravascular ultrasound data. Ultrasound Med Biol 27(10):1319–1331Google Scholar
  64. 64.
    Obaid DR et al (2012) Identification of coronary plaque sub-types using virtual histology intravascular ultrasound is affected by inter-observer variability and differences in plaque definitions. Circ Cardiovasc Imaging 5(1):86–93Google Scholar
  65. 65.
    Papaioannou TG, et al (2012) Quantification of new structural features of coronary plaques by computational post-hoc analysis of virtual histology-intravascular ultrasound images. Computer Methods in Biomechanics and Biomedical Engineering, (ahead-of-print): p. 1–9Google Scholar
  66. 66.
    Papaioannou TG et al (2014) Quantification of new structural features of coronary plaques by computational post-hoc analysis of virtual histology-intravascular ultrasound images. Computer Methods in Biomechanics and Biomedical Engineering 17(6):643–651Google Scholar
  67. 67.
    Peng B, Zhang L, Zhang D (2013) A survey of graph theoretical approaches to image segmentation. Pattern Recogn 46(3):1020–1038Google Scholar
  68. 68.
    Plissiti ME et al (2004) An automated method for lumen and media-adventitia border detection in a sequence of IVUS frames. IEEE Trans Inf Technol Biomed 8(2):131–141Google Scholar
  69. 69.
    Prati F et al (2010) Expert review document on methodology, terminology, and clinical applications of optical coherence tomography: physical principles, methodology of image acquisition, and clinical application for assessment of coronary arteries and atherosclerosis. Eur Heart J 31(4):401–415MathSciNetGoogle Scholar
  70. 70.
    Rezaei Z et al (2016) Automatic Plaque Segmentation based on hybrid Fuzzy Clustering and k Nearest Neighborhood using Virtual Histology Intravascular Ultrasound Images. Appl Soft Comput 53Google Scholar
  71. 71.
    Sales FJR et al (2010) A computational tool for coronary atherosclerotic plaque analysis of Virtual Histology images. In: Computing in Cardiology. IEEEGoogle Scholar
  72. 72.
    Sawada T et al (2008) Feasibility of combined use of intravascular ultrasound radiofrequency data analysis and optical coherence tomography for detecting thin-cap fibroatheroma. Eur Heart J 29(9):1136–1146Google Scholar
  73. 73.
    Schaap M et al (2009) Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Med Image Anal 13(5):701–714Google Scholar
  74. 74.
    Selvathi D, Emimal N, Selvaraj H (2012) Automated Characterization of Atheromatous Plaque in Intravascular Ultrasound Images Using Neuro Fuzzy Classifier. INTL Journal of Electronics and Telecommunications 58(4):425–431Google Scholar
  75. 75.
    Siewiorek GM et al (2012) Reproducibility of IVUS border detection for carotid atherosclerotic plaque assessment. Med Eng Phys 34(6):702–708Google Scholar
  76. 76.
    Siqueira DAdA, et al (2013) Correlation between plaque composition as assessed by virtual histology and C-reactive protein. Arquivos brasileiros de cardiologia, (AHEAD)Google Scholar
  77. 77.
    Siqueira DAdA et al (2013) Correlation between plaque composition as assessed by virtual histology and C-reactive protein. Arq Bras Cardiol 101(1):78–86Google Scholar
  78. 78.
    Sofian H, Ming JTC, Noor NM (2015) Detection of the lumen boundary in the coronary artery disease. in IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). IEEEGoogle Scholar
  79. 79.
    Suh WM et al (2011) Intravascular detection of the vulnerable plaque. Circulation: Cardiovascular Imaging 4(2):169–178Google Scholar
  80. 80.
    Sun S, Sonka M, Beichel RR (2013) Graph-based IVUS segmentation with efficient computer-aided refinement. IEEE Trans Med Imaging 32(8):1536–1549Google Scholar
  81. 81.
    Szczypiński P et al (2014) Texture and color based image segmentation and pathology detection in capsule endoscopy videos. Comput Methods Prog Biomed 113(1):396–411Google Scholar
  82. 82.
    Taki A et al (2008) Automatic segmentation of calcified plaques and vessel borders in IVUS images. Int J Comput Assist Radiol Surg 3(3–4):347–354Google Scholar
  83. 83.
    Taki A et al (2013) An IVUS image-based approach for improvement of coronary plaque characterization. Comput Biol Med 43:268–280Google Scholar
  84. 84.
    Tang D et al (2005) Local maximal stress hypothesis and computational plaque vulnerability index for atherosclerotic plaque assessment. Ann Biomed Eng 33(12):1789–1801Google Scholar
  85. 85.
    Tang D et al (2014) Image-based modeling for better understanding and assessment of atherosclerotic plaque progression and vulnerability: Data, modeling, validation, uncertainty and predictions. J Biomech 47(4):834–846Google Scholar
  86. 86.
    Tarkin JM et al (2016) Imaging atherosclerosis. Circ Res 118(4):750–769Google Scholar
  87. 87.
    Uchino E et al (2012) IVUS-Based Coronary Plaque Tissue Characterization Using Weighted Multiple k-Nearest Neighbor. Eng Lett 20(3):211–216Google Scholar
  88. 88.
    Vachkov G, Uchino E, Nakao S (2012) Moving Window-Based Similarity Analysis and its Application to Tissue Characterization of Coronary Arteries. In: Proceedings of the International MultiConference of Engineers and Computer ScientistsGoogle Scholar
  89. 89.
    Van Soest G et al (2010) Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging. J Biomed Opt 15(1):011105–011105-9Google Scholar
  90. 90.
    Vazquez-Figueroa JG et al (2013) Prospective Validation that Vulnerable Plaque Associated with Major Adverse Outcomes Have Larger Plaque Volume, Less Dense Calcium, and More Non-Calcified Plaque by Quantitative, Three-Dimensional Measurements Using Intravascular Ultrasound with Radiofrequency Backscatter Analysis. J Cardiovasc Transl Res 6(5):762–771Google Scholar
  91. 91.
    Virmani R et al (2000) Lessons from sudden coronary death a comprehensive morphological classification scheme for atherosclerotic lesions. Arterioscler Thromb Vasc Biol 20(5):1262–1275Google Scholar
  92. 92.
    Zhang Q et al (2010) Automatic segmentation of calcifications in intravascular ultrasound images using snakes and the contourlet transform. Ultrasound Med Biol 36(1):111–129Google Scholar
  93. 93.
    Zhang L, et al (2015) Prospective prediction of Thin-Cap Fibroatheromas from baseline Virtual Histology Intravascular Ultrasound data, in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015. Springer. p. 603–610.Google Scholar
  94. 94.
    Zhang L et al (2016) Location-specific prediction of vulnerable plaque using IVUS, virtual histology, and spatial context. in IEEE 13th International Symposium on Biomedical Imaging (ISBI). IEEEGoogle Scholar
  95. 95.
    Zhao F, Xie X, Roach M (2015) Computer Vision Techniques for Transcatheter Intervention. IEEE Journal of Translational Engineering in Health and Medicine 3:1–31Google Scholar
  96. 96.
    Zhao Z et al (2015) Fibroatheroma Morphological Features of Borderline Coronary Lesion Plaques on Stable Angina Pectoris Patients. Enliven: Clinical Cardiology and Research 2(1):002Google Scholar
  97. 97.
    Zhu X et al (2011) A snake-based method for segmentation of intravascular ultrasound images and its in vivo validation. Ultrasonics 51(2):181–189Google Scholar
  98. 98.
    Zimarino M et al (2016) The value of imaging in subclinical coronary artery disease. Vasc Pharmacol 82:20–29Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Zahra Rezaei
    • 1
    • 2
    Email author
  • Ali Selamat
    • 2
    • 3
  • Arash Taki
    • 4
  • Mohd Shafry Mohd Rahim
    • 2
  • Mohammed Rafiq Abdul Kadir
    • 5
  1. 1.Islamic Azad UniversityDepartment of Computer Engineering, Marvdasht BranchMarvdashtIran
  2. 2.Faculty of ComputingUniversiti Teknologi Malaysia (UTM) & UTM-IRDA Center of ExcellenceJohor BahruMalaysia
  3. 3.University of Hradec KraloveHradec KraloveCzech Republic
  4. 4.Technical University of Munich (TUM)MunichGermany
  5. 5.Faculty of Biosciences & Medical EngineeringUniversiti Teknologi MalaysiaJohor BahruMalaysia

Personalised recommendations