Skip to main content

Advertisement

Log in

High Risk Plaque Features on Coronary CT Angiography

  • Cardiac Computed Tomography (S Achenbach and T Villines, Section Editor)
  • Published:
Current Cardiovascular Imaging Reports Aims and scope Submit manuscript

Abstract

Coronary computed tomography angiography (CCTA) is a non-invasive imaging technique that can detect, characterize and quantify coronary atherosclerotic plaques in routine clinical settings. The distinct morphological features of vulnerable plaques and stable lesions provide an opportunity for CCTA to identify high-risk plaque features and guide stratified therapeutic interventions. Morphological plaque characteristics, such as large plaque volume, positive remodelling, low CT attenuation, spotty calcification and the napkin-ring sign have been linked to elevated risk of acute coronary syndrome. Recent advances in computational fluid dynamics enabled functional plaque assessment through endothelial shear stress and lesion specific fractional flow reserve calculation. The comprehensive, morphological and functional plaque assessment may improve the identification of vulnerable coronary lesions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: •• Of major importance

  1. Go AS et al. Heart disease and stroke statistics–2014 update: a report from the American Heart Association. Circulation. 2014;129(3):e28–e292.

    PubMed  Google Scholar 

  2. Falk E. Pathogenesis of atherosclerosis. J Am Coll Cardiol. 2006;47(8 Suppl):C7–C12.

    CAS  PubMed  Google Scholar 

  3. Hansson GK. Inflammation, atherosclerosis, and coronary artery disease. N Engl J Med. 2005;352(16):1685–95.

    CAS  PubMed  Google Scholar 

  4. Libby P. Atherosclerosis: the new view. Sci Am. 2002;286(5):46–55.

    PubMed  Google Scholar 

  5. Burke AP et al. Coronary risk factors and plaque morphology in men with coronary disease who died suddenly. N Engl J Med. 1997;336(18):1276–82.

    CAS  PubMed  Google Scholar 

  6. Falk E et al. Update on acute coronary syndromes: the pathologists’ view. Eur Heart J. 2013;34(10):719–28.

    CAS  PubMed  Google Scholar 

  7. Virmani R et al. Lessons from sudden coronary death: a comprehensive morphological classification scheme for atherosclerotic lesions. Arterioscler Thromb Vasc Biol. 2000;20(5):1262–75.

    CAS  PubMed  Google Scholar 

  8. Narula J, Achenbach S. Napkin-ring necrotic cores: defining circumferential extent of necrotic cores in unstable plaques. J Am Coll Cardiol Img. 2009;2(12):1436–8.

    Google Scholar 

  9. Alkadhi H et al. Low-dose, 128-slice, dual-source CT coronary angiography: accuracy and radiation dose of the high-pitch and the step-and-shoot mode. Heart. 2010;96(12):933–8.

    PubMed  Google Scholar 

  10. Sun Z, Ng KH. Diagnostic value of coronary CT angiography with prospective ECG-gating in the diagnosis of coronary artery disease: a systematic review and meta-analysis. Int J Cardiovasc Imaging. 2012;28(8):2109–19.

    PubMed  Google Scholar 

  11. Miller JM et al. Diagnostic performance of coronary angiography by 64-row CT. N Engl J Med. 2008;359(22):2324–36.

    CAS  PubMed  Google Scholar 

  12. Muller JE, Tofler GH, Stone PH. Circadian variation and triggers of onset of acute cardiovascular disease. Circulation. 1989;79(4):733–43.

    CAS  PubMed  Google Scholar 

  13. Virmani R et al. Pathology of the vulnerable plaque. J Am Coll Cardiol. 2006;47(8 Suppl):C13–8.

    CAS  PubMed  Google Scholar 

  14. van der Giessen AG et al. Small coronary calcifications are not detectable by 64-slice contrast enhanced computed tomography. Int J Cardiovasc Imaging. 2011;27(1):143–52.

    PubMed Central  PubMed  Google Scholar 

  15. Stone GW et al. A prospective natural-history study of coronary atherosclerosis. N Engl J Med. 2011;364(3):226–35.

    CAS  PubMed  Google Scholar 

  16. Maurovich-Horvat P et al. Methods of plaque quantification and characterization by cardiac computed tomography. J Cardiovasc Comput Tomogr. 2009;3 Suppl 2:S91–8.

    PubMed  Google Scholar 

  17. Schepis T et al. Comparison of dual source computed tomography versus intravascular ultrasound for evaluation of coronary arteries at least one year after cardiac transplantation. Am J Cardiol. 2009;104(10):1351–6.

    PubMed  Google Scholar 

  18. Brodoefel H et al. Coronary plaque quantification by voxel analysis: dual-source MDCT angiography versus intravascular sonography. AJR Am J Roentgenol. 2009;192(3):W84–9.

    PubMed Central  PubMed  Google Scholar 

  19. Petranovic M et al. Assessment of nonstenotic coronary lesions by 64-slice multidetector computed tomography in comparison to intravascular ultrasound: evaluation of nonculprit coronary lesions. J Cardiovasc Comput Tomogr. 2009;3(1):24–31.

    PubMed  Google Scholar 

  20. Sun J et al. Identification and quantification of coronary atherosclerotic plaques: a comparison of 64-MDCT and intravascular ultrasound. AJR Am J Roentgenol. 2008;190(3):748–54.

    PubMed  Google Scholar 

  21. Voros S et al. Coronary atherosclerosis imaging by coronary CT angiography: current status, correlation with intravascular interrogation and meta-analysis. J Am Coll Cardiol Img. 2011;4(5):537–48. A comprehensive review on the CCTA based quantitative and qualitative plaque characterization.

    Google Scholar 

  22. Leber AW et al. Accuracy of 64-slice computed tomography to classify and quantify plaque volumes in the proximal coronary system: a comparative study using intravascular ultrasound. J Am Coll Cardiol. 2006;47(3):672–7.

    PubMed  Google Scholar 

  23. Hur J et al. Quantification and characterization of obstructive coronary plaques using 64-slice computed tomography: a comparison with intravascular ultrasound. J Comput Assist Tomogr. 2009;33(2):186–92.

    PubMed  Google Scholar 

  24. Boogers MJ et al. Automated quantification of coronary plaque with computed tomography: comparison with intravascular ultrasound using a dedicated registration algorithm for fusion-based quantification. Eur Heart J. 2012;33(8):1007–16. This study demonstrated the feasibility of semiautomated quantification of coronary plaque burden with CCTA. Quantitative computed tomography and IVUS showed good correlation.

    PubMed  Google Scholar 

  25. Voros S et al. Prospective validation of standardized, 3-dimensional, quantitative coronary computed tomographic plaque measurements using radiofrequency backscatter intravascular ultrasound as reference standard in intermediate coronary arterial lesions: results from the ATLANTA (assessment of tissue characteristics, lesion morphology, and hemodynamics by angiography with fractional flow reserve, intravascular ultrasound and virtual histology, and noninvasive computed tomography in atherosclerotic plaques) I study. J Am Coll Cardiol Intv. 2011;4(2):198–208.

    Google Scholar 

  26. Oberoi S et al. Reproducibility of non-calcified coronary artery plaque burden quantification from coronary CT angiography across different image analysis platforms. AJR Am J Roentgenol. 2014;202(1):W43–9.

    PubMed  Google Scholar 

  27. Pflederer T et al. Characterization of culprit lesions in acute coronary syndromes using coronary dual-source CT angiography. Atherosclerosis. 2010;211(2):437–44.

    CAS  PubMed  Google Scholar 

  28. Madder RD et al. Features of disrupted plaques by coronary computed tomographic angiography: correlates with invasively proven complex lesions. Circ Cardiovasc Imaging. 2011;4(2):105–13.

    PubMed  Google Scholar 

  29. Motoyama S et al. Computed tomographic angiography characteristics of atherosclerotic plaques subsequently resulting in acute coronary syndrome. J Am Coll Cardiol. 2009;54(1):49–57.

    PubMed  Google Scholar 

  30. Kristensen TS et al. Prognostic implications of nonobstructive coronary plaques in patients with non-ST-segment elevation myocardial infarction: a multidetector computed tomography study. J Am Coll Cardiol. 2011;58(5):502–9. This prospective study demonstrated that the total amount of non-calcified plaque independently associated with an increased risk of recurrent coronary events after NSTEMI.

    PubMed  Google Scholar 

  31. Versteylen MO et al. Additive value of semiautomated quantification of coronary artery disease using cardiac computed tomographic angiography to predict future acute coronary syndrome. J Am Coll Cardiol. 2013;61(22):2296–305.

    PubMed  Google Scholar 

  32. Narula J et al. Arithmetic of vulnerable plaques for noninvasive imaging. Nat Clin Pract Cardiovasc Med. 2008;5 Suppl 2:S2–S10.

    PubMed  Google Scholar 

  33. Kopp AF et al. Non-invasive characterisation of coronary lesion morphology and composition by multislice CT: first results in comparison with intracoronary ultrasound. Eur Radiol. 2001;11(9):1607–11.

    CAS  PubMed  Google Scholar 

  34. Schroeder S et al. Noninvasive detection and evaluation of atherosclerotic coronary plaques with multislice computed tomography. J Am Coll Cardiol. 2001;37(5):1430–5.

    CAS  PubMed  Google Scholar 

  35. Becker CR et al. Ex vivo coronary atherosclerotic plaque characterization with multi-detector-row CT. Eur Radiol. 2003;13(9):2094–8.

    PubMed  Google Scholar 

  36. Leber AW et al. Accuracy of multidetector spiral computed tomography in identifying and differentiating the composition of coronary atherosclerotic plaques: a comparative study with intracoronary ultrasound. J Am Coll Cardiol. 2004;43(7):1241–7.

    PubMed  Google Scholar 

  37. Pohle K et al. Characterization of non-calcified coronary atherosclerotic plaque by multi-detector row CT: comparison to IVUS. Atherosclerosis. 2007;190(1):174–80.

    CAS  PubMed  Google Scholar 

  38. Ferencik M et al. Arterial wall imaging: evaluation with 16-section multidetector CT in blood vessel phantoms and ex vivo coronary arteries. Radiology. 2006;240(3):708–16.

    PubMed  Google Scholar 

  39. Gauss S et al. Assessment of coronary artery remodelling by dual-source CT: a head-to-head comparison with intravascular ultrasound. Heart. 2011;97(12):991–7.

    PubMed  Google Scholar 

  40. Schlett CL et al. Histogram analysis of lipid-core plaques in coronary computed tomographic angiography: ex vivo validation against histology. Investig Radiol. 2013;48(9):646–53.

    Google Scholar 

  41. Kashiwagi M et al. Feasibility of noninvasive assessment of thin-cap fibroatheroma by multidetector computed tomography. J Am Coll Cardiol Img. 2009;2(12):1412–9.

    Google Scholar 

  42. Ito T et al. Comparison of in vivo assessment of vulnerable plaque by 64-slice multislice computed tomography versus optical coherence tomography. Am J Cardiol. 2011;107(9):1270–7.

    PubMed  Google Scholar 

  43. Ito H, et al. Characteristics of plaque progression detected by serial coronary computed tomography angiography. Heart and vessels, 2013. doi:10.1007/s00380-013-0420-4.

  44. Viles-Gonzalez JF et al. In vivo 16-slice, multidetector-row computed tomography for the assessment of experimental atherosclerosis: comparison with magnetic resonance imaging and histopathology. Circulation. 2004;110(11):1467–72.

    PubMed  Google Scholar 

  45. Achenbach S et al. Influence of slice thickness and reconstruction kernel on the computed tomographic attenuation of coronary atherosclerotic plaque. J Cardiovasc Comput Tomogr. 2010;4(2):110–5.

    PubMed  Google Scholar 

  46. Cademartiri F et al. Influence of intracoronary attenuation on coronary plaque measurements using multislice computed tomography: observations in an ex vivo model of coronary computed tomography angiography. Eur Radiol. 2005;15(7):1426–31.

    PubMed  Google Scholar 

  47. Suzuki S et al. Accuracy of attenuation measurement of vascular wall in vitro on computed tomography angiography: effect of wall thickness, density of contrast medium, and measurement point. Investig Radiol. 2006;41(6):510–5.

    Google Scholar 

  48. Achenbach S et al. CV imaging: what was new in 2012? JACC Cardiovasc Imaging. 2013;6(6):714–34.

    PubMed  Google Scholar 

  49. Motoyama S et al. Multislice computed tomographic characteristics of coronary lesions in acute coronary syndromes. J Am Coll Cardiol. 2007;50(4):319–26.

    PubMed  Google Scholar 

  50. Kim SY et al. The culprit lesion score on multi-detector computed tomography can detect vulnerable coronary artery plaque. Int J Cardiovasc Imaging. 2010;26 Suppl 2:245–52.

    PubMed  Google Scholar 

  51. Kitagawa T et al. Characterization of non-calcified coronary plaques and identification of culprit lesions in patients with acute coronary syndrome by 64-slice computed tomography. JACC Cardiovasc Imaging. 2009;2(2):153–60.

    PubMed  Google Scholar 

  52. Glagov S et al. Compensatory enlargement of human atherosclerotic coronary arteries. N Engl J Med. 1987;316(22):1371–5.

    CAS  PubMed  Google Scholar 

  53. Narula J, Strauss HW. The popcorn plaques. Nat Med. 2007;13(5):532–4.

    CAS  PubMed  Google Scholar 

  54. Libby P. Mechanisms of acute coronary syndromes and their implications for therapy. N Engl J Med. 2013;368(21):2004–13. A comprehensive overview of the pathophysiology and latest therapeutic implications of acut coronary syndrome.

  55. Achenbach S et al. Assessment of coronary remodelling in stenotic and nonstenotic coronary atherosclerotic lesions by multidetector spiral computed tomography. J Am Coll Cardiol. 2004;43(5):842–7.

    PubMed  Google Scholar 

  56. Mintz GS et al. 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. 2001;37(5):1478–92.

    CAS  PubMed  Google Scholar 

  57. Kroner ES et al. Positive remodelling on coronary computed tomography as a marker for plaque vulnerability on virtual histology intravascular ultrasound. Am J Cardiol. 2011;107(12):1725–9.

    PubMed  Google Scholar 

  58. Otsuka F, Finn AV, Virmani R. Do vulnerable and ruptured plaques hide in heavily calcified arteries? Atherosclerosis. 2013;229(1):34–7.

    CAS  PubMed  Google Scholar 

  59. Nakazawa G et al. Efficacy of culprit plaque assessment by 64-slice multidetector computed tomography to predict transient no-reflow phenomenon during percutaneous coronary intervention. Am Heart J. 2008;155(6):1150–7.

    PubMed  Google Scholar 

  60. Taylor AJ et al. Coronary calcium independently predicts incident premature coronary heart disease over measured cardiovascular risk factors: mean three-year outcomes in the Prospective Army Coronary Calcium (PACC) project. J Am Coll Cardiol. 2005;46(5):807–14.

    CAS  PubMed  Google Scholar 

  61. Greenland P et al. Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals. JAMA. 2004;291(2):210–5.

    CAS  PubMed  Google Scholar 

  62. Huang H et al. The impact of calcification on the biomechanical stability of atherosclerotic plaques. Circulation. 2001;103(8):1051–6.

    CAS  PubMed  Google Scholar 

  63. Mauriello A et al. Coronary calcification identifies the vulnerable patient rather than the vulnerable Plaque. Atherosclerosis. 2013;229(1):124–9.

    CAS  PubMed  Google Scholar 

  64. Maldonado N et al. A mechanistic analysis of the role of microcalcifications in atherosclerotic plaque stability: potential implications for plaque rupture. Am J Physiol Heart Circ Physiol. 2012;303(5):H619–28.

    CAS  PubMed Central  PubMed  Google Scholar 

  65. Kataoka Y et al. Spotty calcification as a marker of accelerated progression of coronary atherosclerosis: insights from serial intravascular ultrasound. J Am Coll Cardiol. 2012;59(18):1592–7.

    PubMed  Google Scholar 

  66. Ehara S et al. Spotty calcification typifies the culprit plaque in patients with acute myocardial infarction: an intravascular ultrasound study. Circulation. 2004;110(22):3424–9.

    PubMed  Google Scholar 

  67. Ferencik M et al. A computed tomography-based coronary lesion score to predict acute coronary syndrome among patients with acute chest pain and significant coronary stenosis on coronary computed tomographic angiogram. Am J Cardiol. 2012;110(2):183–9.

    PubMed Central  PubMed  Google Scholar 

  68. van Velzen JE et al. Comprehensive assessment of spotty calcifications on computed tomography angiography: comparison to plaque characteristics on intravascular ultrasound with radiofrequency backscatter analysis. J Nucl Cardiol Off Publ Am Soc Nucl Cardiol. 2011;18(5):893–903.

    Google Scholar 

  69. Ozaki Y et al. Coronary CT angiographic characteristics of culprit lesions in acute coronary syndromes not related to plaque rupture as defined by optical coherence tomography and angioscopy. Eur Heart J. 2011;32(22):2814–23.

    PubMed  Google Scholar 

  70. Narula J et al. Histopathologic characteristics of atherosclerotic coronary disease and implications of the findings for the invasive and noninvasive detection of vulnerable plaques. J Am Coll Cardiol. 2013;61(10):1041–51. This postmortem study analized 295 coronary atherosclerotic plaques to define histomorphologic characteristics of vulnerable plaques and suggests that plaques that rupture cause substantial luminal narrowing before the acute event.

    PubMed Central  PubMed  Google Scholar 

  71. Kodama T et al. Computed tomographic angiography-verified plaque characteristics and slow-flow phenomenon during percutaneous coronary intervention. JACC Cardiovasc Interv. 2012;5(6):636–43.

    PubMed  Google Scholar 

  72. Tanaka A et al. Non-invasive assessment of plaque rupture by 64-slice multidetector computed tomography–comparison with intravascular ultrasound. Circ J. 2008;72(8):1276–81.

    PubMed  Google Scholar 

  73. Maurovich-Horvat P et al. The napkin-ring sign: CT signature of high-risk coronary plaques? JACC. Cardiovascular Imaging. 2010;3(4):440–4.

    PubMed  Google Scholar 

  74. Maurovich-Horvat P et al. The napkin-ring sign indicates advanced atherosclerotic lesions in coronary CT angiography. J Am Coll Cardiol Img. 2012;5(12):1243–52.

    Google Scholar 

  75. Seifarth H et al. Histopathological correlates of the napkin-ring sign plaque in coronary CT angiography. Atherosclerosis. 2012;224(1):90–6.

    CAS  PubMed  Google Scholar 

  76. Otsuka K et al. Napkin-ring sign on coronary CT angiography for the prediction of acute coronary syndrome. J Am Coll Cardiol Img. 2013;6(4):448–57. The first prospective study which demonstrated that the napkin-ring sign on the CCTA is strongly associated with future ACS events.

    Google Scholar 

  77. Achenbach S. Computed tomography coronary angiography. J Am Coll Cardiol. 2006;48(10):1919–28.

    PubMed  Google Scholar 

  78. Meijboom WB et al. Comprehensive assessment of coronary artery stenoses: computed tomography coronary angiography versus conventional coronary angiography and correlation with fractional flow reserve in patients with stable angina. J Am Coll Cardiol. 2008;52(8):636–43.

    PubMed  Google Scholar 

  79. De Bruyne B et al. Coronary flow reserve calculated from pressure measurements in humans. Validation with positron emission tomography. Circulation. 1994;89(3):1013–22.

    PubMed  Google Scholar 

  80. Berger A et al. Long-term clinical outcome after fractional flow reserve-guided percutaneous coronary intervention in patients with multivessel disease. J Am Coll Cardiol. 2005;46(3):438–42.

    PubMed  Google Scholar 

  81. Tonino PA et al. Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. N Engl J Med. 2009;360(3):213–24.

    CAS  PubMed  Google Scholar 

  82. Pijls NH et al. Experimental basis of determining maximum coronary, myocardial, and collateral blood flow by pressure measurements for assessing functional stenosis severity before and after percutaneous transluminal coronary angioplasty. Circulation. 1993;87(4):1354–67.

    CAS  PubMed  Google Scholar 

  83. Koo BK 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.

    PubMed  Google Scholar 

  84. Bech GJ et al. Fractional flow reserve to determine the appropriateness of angioplasty in moderate coronary stenosis: a randomized trial. Circulation. 2001;103(24):2928–34.

    CAS  PubMed  Google Scholar 

  85. Fearon WF. Is a myocardial infarction more likely to result from a mild coronary lesion or an ischemia-producing one? Circ Cardiovasc Interv. 2011;4(6):539–41.

    PubMed  Google Scholar 

  86. Gijsen FJ 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.

    CAS  PubMed  Google Scholar 

  87. Hachamovitch R et al. Value of stress myocardial perfusion single photon emission computed tomography in patients with normal resting electrocardiograms: an evaluation of incremental prognostic value and cost-effectiveness. Circulation. 2002;105(7):823–9.

    PubMed  Google Scholar 

  88. Shaw LJ et al. Optimal medical therapy with or without percutaneous coronary intervention to reduce ischemic burden: results from the Clinical Outcomes Utilizing Revascularization and Aggressive Drug Evaluation (COURAGE) trial nuclear substudy. Circulation. 2008;117(10):1283–91.

    PubMed  Google Scholar 

  89. Caro CG, Fitz-Gerald JM, Schroter RC. Arterial wall shear and distribution of early atheroma in man. Nature. 1969;223(5211):1159–60.

    CAS  PubMed  Google Scholar 

  90. Friedman MH et al. Correlation between wall shear and intimal thickness at a coronary artery branch. Atherosclerosis. 1987;68(1–2):27–33.

    CAS  PubMed  Google Scholar 

  91. Koskinas KC et al. Natural history of experimental coronary atherosclerosis and vascular remodelling in relation to endothelial shear stress: a serial, in vivo intravascular ultrasound study. Circulation. 2010;121(19):2092–101.

    PubMed Central  PubMed  Google Scholar 

  92. Malek AM, Alper SL, Izumo S. Hemodynamic shear stress and its role in atherosclerosis. JAMA. 1999;282(21):2035–42.

    CAS  PubMed  Google Scholar 

  93. Wentzel JJ et al. 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.

    CAS  PubMed  Google Scholar 

  94. Chatzizisis YS et al. Role of endothelial shear stress in the natural history of coronary atherosclerosis and vascular remodelling: molecular, cellular, and vascular behavior. J Am Coll Cardiol. 2007;49(25):2379–93.

    CAS  PubMed  Google Scholar 

  95. Dey D et al. Automated three-dimensional quantification of non-calcified coronary plaque from coronary CT angiography: comparison with intravascular US. Radiology. 2010;257(2):516–22.

    PubMed  Google Scholar 

  96. Stone PH et al. Prediction of progression of coronary artery disease and clinical outcomes using vascular profiling of endothelial shear stress and arterial plaque characteristics: the PREDICTION Study. Circulation. 2012;126(2):172–81.

    PubMed  Google Scholar 

  97. Frauenfelder T et al. In-vivo flow simulation in coronary arteries based on computed tomography datasets: feasibility and initial results. Eur Radiol. 2007;17(5):1291–300.

    PubMed  Google Scholar 

  98. Jin S et al. Flow patterns and wall shear stress distributions at atherosclerotic-prone sites in a human left coronary artery–an exploration using combined methods of CT and computational fluid dynamics. Conf Proc IEEE Eng Med Biol Soc. 2004;5:3789–91.

    PubMed Central  PubMed  Google Scholar 

  99. Borkin MA et al. Evaluation of artery visualizations for heart disease diagnosis. IEEE Trans Vis Comput Graph. 2011;17(12):2479–88.

    PubMed  Google Scholar 

  100. Ramkumar PG et al. New advances in cardiac computed tomography. Curr Opin Cardiol. 2009;24(6):596–603.

    PubMed  Google Scholar 

  101. Gijsen FJ et al. 3D reconstruction techniques of human coronary bifurcations for shear stress computations. J Biomech. 2014;47(1):39–43.

    PubMed  Google Scholar 

  102. McCullough EC. Photon attenuation in computed tomography. Med Phys. 1975;2(6):307–20.

    CAS  PubMed  Google Scholar 

  103. Alvarez RE, Macovski A. Energy-selective reconstructions in X-ray computerized tomography. Phys Med Biol. 1976;21(5):733–44.

    CAS  PubMed  Google Scholar 

  104. Halliburton SS. Recent technologic advances in multi-detector row cardiac CT. Cardiol Clin. 2009;27(4):655–64.

    PubMed  Google Scholar 

  105. Ruzsics B et al. Dual-energy CT of the heart for diagnosing coronary artery stenosis and myocardial ischemia-initial experience. Eur Radiol. 2008;18(11):2414–24.

    PubMed  Google Scholar 

  106. Schwarz F et al. Dual-energy CT of the heart–principles and protocols. Eur J Radiol. 2008;68(3):423–33.

    PubMed  Google Scholar 

  107. So A et al. Prospectively ECG-triggered rapid kV-switching dual-energy CT for quantitative imaging of myocardial perfusion. JACC Cardiovasc Imaging. 2012;5(8):829–36.

    PubMed  Google Scholar 

  108. Shinohara M et al. Atherosclerotic plaque imaging using phase-contrast X-ray computed tomography. Am J Physiol Heart Circ Physiol. 2008;294(2):H1094–100.

    CAS  PubMed  Google Scholar 

  109. Momose A, Fukuda J. Phase-contrast radiographs of nonstained rat cerebellar specimen. Med Phys. 1995;22(44):6355–67.

    Google Scholar 

  110. Fitzgerald R. Phase-sensitive x-ray imaging. Phys Today. 2000;53(7):23–6.

    Google Scholar 

  111. Hyafil F et al. Quantification of inflammation within rabbit atherosclerotic plaques using the macrophage-specific CT contrast agent N1177: a comparison with 18F-FDG PET/CT and histology. J Nucl Med. 2009;50(6):959–65.

    CAS  PubMed  Google Scholar 

  112. Cormode DP et al. Atherosclerotic plaque composition: analysis with multicolor CT and targeted gold nanoparticles. Radiology. 2010;256(3):774–82.

    PubMed Central  PubMed  Google Scholar 

  113. Rogers IS et al. Feasibility of FDG imaging of the coronary arteries: comparison between acute coronary syndrome and stable angina. JACC Cardiovasc Imaging. 2010;3(4):388–97.

    PubMed  Google Scholar 

  114. Joshi NV et al. 18F-fluoride positron emission tomography for identification of ruptured and high-risk coronary atherosclerotic plaques: a prospective clinical trial. Lancet. 2014;383(9918):705–13. In this prospective clinical trial the authors have shown that intense 18F-NaF uptake localises to recent plaque rupture in patients with acute myocardial infarction.

  115. Marin D et al. Low-tube-voltage, high-tube-current multidetector abdominal CT: improved image quality and decreased radiation dose with adaptive statistical iterative reconstruction algorithm–initial clinical experience. Radiology. 2010;254(1):145–53.

    PubMed  Google Scholar 

  116. Prakash P et al. Diffuse lung disease: CT of the chest with adaptive statistical iterative reconstruction technique. Radiology. 2010;256(1):261–9.

    PubMed  Google Scholar 

  117. Yu Z et al. Fast model-based X-ray CT reconstruction using spatially nonhomogeneous ICD optimization. IEEE Trans Image Process. 2011;20(1):161–75.

    PubMed  Google Scholar 

  118. Scheffel H et al. Coronary artery plaques: cardiac CT with model-based and adaptive-statistical iterative reconstruction technique. Eur J Radiol. 2012;81(3):e363–9.

    PubMed  Google Scholar 

  119. Yoo RE et al. Image quality of adaptive iterative dose reduction 3D of coronary CT angiography of 640-slice CT: comparison with filtered back-projection. Int J Cardiovasc Imaging. 2013;29(3):669–76.

    PubMed  Google Scholar 

  120. Leipsic J, Heilbron BG, Hague C. Iterative reconstruction for coronary CT angiography: finding its way. Int J Cardiovasc Imaging. 2012;28(3):613–20.

    PubMed  Google Scholar 

  121. Benedek T, Gyongyosi M, Benedek I. Multislice computed tomographic coronary angiography for quantitative assessment of culprit lesions in acute coronary syndromes. Can J Cardiol. 2013;29(3):364–71.

    PubMed  Google Scholar 

  122. Leipsic J et al. Estimated radiation dose reduction using adaptive statistical iterative reconstruction in coronary CT angiography: the ERASIR study. AJR Am J Roentgenol. 2010;195(3):655–60.

    PubMed  Google Scholar 

  123. Min JK et al. Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA. 2012;308(12):1237–45.

    CAS  PubMed  Google Scholar 

  124. Takx RA et al. The effect of iterative reconstruction on quantitative computed tomography assessment of coronary plaque composition. Int J Cardiovasc Imaging. 2014;30(1):155–63.

    PubMed  Google Scholar 

  125. Fuchs TA et al. CT coronary angiography: impact of adapted statistical iterative reconstruction (ASIR) on coronary stenosis and plaque composition analysis. Int J Cardiovasc Imaging. 2013;29(3):719–24.

    PubMed  Google Scholar 

  126. Renker M et al. Evaluation of heavily calcified vessels with coronary CT angiography: comparison of iterative and filtered back projection image reconstruction. Radiology. 2011;260(2):390–9.

    PubMed  Google Scholar 

  127. Puchner SB et al. The effect of iterative image reconstruction algorithms on the feasibility of automated plaque assessment in coronary CT angiography. Int J Cardiovasc Imaging. 2013;29(8):1879–88.

    PubMed  Google Scholar 

Download references

Acknowledgments

The authors thank Rolf Raaijmakers for the images processed with model based iterative reconstruction. This work was supported by the European Union and the State of Hungary, co-financed by the European Social Fund in the framework of TÁMOP 4.2.4. A/1-11-1-2012-0001 ‘National Excellence Program’.

Compliance with Ethics Guidelines

Conflict of Interest

Andrea Bartykowszki, Csilla Celeng, Mihály Károlyi, and Pál Maurovich-Horvat declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pál Maurovich-Horvat.

Additional information

This article is part of the Topical Collection on Cardiac Computed Tomography

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bartykowszki, A., Celeng, C., Károlyi, M. et al. High Risk Plaque Features on Coronary CT Angiography. Curr Cardiovasc Imaging Rep 7, 9279 (2014). https://doi.org/10.1007/s12410-014-9279-8

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s12410-014-9279-8

Keywords

Navigation