Skip to main content

Advertisement

Log in

Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology

  • Original Paper
  • Published:
The International Journal of Cardiovascular Imaging Aims and scope Submit manuscript

Abstract

Plaque constitution on computed tomography coronary angiography (CTA) is associated with prognosis. At present only visual assessment of plaque constitution is possible. An accurate automatic, quantitative approach for CTA plaque constitution assessment would improve reproducibility and allows higher accuracy. The present study assessed the feasibility of a fully automatic and quantitative analysis of atherosclerosis on CTA. Clinically derived CTA and intravascular ultrasound virtual histology (IVUS VH) datasets were used to investigate the correlation between quantitatively automatically derived CTA parameters and IVUS VH. A total of 57 patients underwent CTA prior to IVUS VH. First, quantitative CTA quantitative computed tomography (QCT) was performed. Per lesion stenosis parameters and plaque volumes were assessed. Using predefined HU thresholds, CTA plaque volume was differentiated in 4 different plaque types necrotic core (NC), dense calcium (DC), fibrotic (FI) and fibro-fatty tissue (FF). At the identical level of the coronary, the same parameters were derived from IVUS VH. Bland–Altman analyses were performed to assess the agreement between QCT and IVUS VH. Assessment of plaque volume using QCT in 108 lesions showed excellent correlation with IVUS VH (r = 0.928, p < 0.001) (Fig. 1). The correlation of both FF and FI volume on IVUS VH and QCT was good (r = 0.714, p < 0.001 and r = 0.695, p < 0.001 respectively) with corresponding bias and 95 % limits of agreement of 24 mm3 (−42; 90) and 7.7 mm3 (−54; 70). Furthermore, NC and DC were well-correlated in both modalities (r = 0.523, p < 0.001) and (r = 0.736, p < 0.001). Automatic, quantitative CTA tissue characterization is feasible using a dedicated software tool.

Schematic illustration of the characterization of coronary plaque on CTA: cross-correlation with IVUS VH. First, the 3-dimensional centerline was generated from the CTA data set using an automatic tree extraction algorithm (Panel I). Using a unique registration a complete pullback series of IVUS images was mapped on the CTA volume using true anatomical markers (Panel II). Fully automatic lumen and vessel wall contour detection was performed for both imaging modalities (Panel III). Finally, fusion-based quantification of atherosclerotic lesions was based on the lumen and vessel wall contours as well as the corresponding reference lines (estimate of normal tapering of the coronary artery), as shown in panel IV. At the level of the minimal lumen area (MLA) (yellow lines), stenosis parameters, could be calculated for both imaging techniques. Additionally, plaque volumes and plaque types were derived for the whole coronary artery lesion, ranging from the proximal to distal lesion marker (blue markers). Fibrotic tissue was labeled in dark green, Fibro-fatty tissue in light green, dense calcium in white and necrotic core was labeled in red

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. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Budoff MJ, Dowe D, Jollis JG, Gitter M, Sutherland J, Halamert E, Scherer M, Bellinger R, Martin A, Benton R, Delago A, Min JK (2008) Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (assessment by coronary computed tomographic angiography of individuals undergoing invasive coronary angiography) trial. J Am Coll Cardiol 52(21):1724–1732

    Article  PubMed  Google Scholar 

  2. Miller JM, Rochitte CE, Dewey M, Arbab-Zadeh A, Niinuma H, Gottlieb I, Paul N, Clouse ME, Shapiro EP, Hoe J, Lardo AC, Bush DE, de Roos A, Cox C, Brinker J, Lima JA (2008) Diagnostic performance of coronary angiography by 64-row CT. N Engl J Med 359(22):2324–2336

    Article  PubMed  CAS  Google Scholar 

  3. Chow BJ, Small G, Yam Y, Chen L, Achenbach S, Al-Mallah M, Berman DS, Budoff MJ, Cademartiri F, Callister TQ, Chang HJ, Cheng V, Chinnaiyan KM, Delago A, Dunning A, Hadamitzky M, Hausleiter J, Kaufmann P, Lin F, Maffei E, Raff GL, Shaw LJ, Villines TC, Min JK (2011) Incremental prognostic value of cardiac computed tomography in coronary artery disease using CONFIRM: COroNary computed tomography angiography evaluation for clinical outcomes: an InteRnational Multicenter registry. Circ Cardiovasc Imaging 4(5):463–472

    Article  PubMed  Google Scholar 

  4. Min JK, Shaw LJ, Devereux RB, Okin PM, Weinsaft JW, Russo DJ, Lippolis NJ, Berman DS, Callister TQ (2007) Prognostic value of multidetector coronary computed tomographic angiography for prediction of all-cause mortality. J Am Coll Cardiol 50(12):1161–1170

    Article  PubMed  Google Scholar 

  5. Min JK, Feignoux J, Treutenaere J, Laperche T, Sablayrolles J (2010) The prognostic value of multidetector coronary CT angiography for the prediction of major adverse cardiovascular events: a multicenter observational cohort study. Int J Cardiovasc Imaging 26(6):721–728

    Article  PubMed  Google Scholar 

  6. Motoyama S, Sarai M, Harigaya H, Anno H, Inoue K, Hara T, Naruse H, Ishii J, Hishida H, Wong ND, Virmani R, Kondo T, Ozaki Y, Narula J (2009) Computed tomographic angiography characteristics of atherosclerotic plaques subsequently resulting in acute coronary syndrome. J Am Coll Cardiol 54(1):49–57

    Article  PubMed  Google Scholar 

  7. Brodoefel H, Burgstahler C, Heuschmid M, Reimann A, Khosa F, Kopp A, Schroeder S, Claussen CD, Clouse ME (2009) Accuracy of dual-source CT in the characterisation of non-calcified plaque: use of a colour-coded analysis compared with virtual histology intravascular ultrasound. Br J Radiol 82(982):805–812

    Article  PubMed  CAS  Google Scholar 

  8. Otsuka M, Bruining N, Van Pelt NC, Mollet NR, Ligthart JM, Vourvouri E, Hamers R, De JP, Wijns W, Van Domburg RT, Stone GW, Veldhof S, Verheye S, Dudek D, Serruys PW, Krestin GP, De Feyter PJ (2008) Quantification of coronary plaque by 64-slice computed tomography: a comparison with quantitative intracoronary ultrasound. Invest Radiol 43(5):314–321

    Article  PubMed  Google Scholar 

  9. Nair A, Kuban BD, Tuzcu EM, Schoenhagen P, Nissen SE, Vince DG (2002) Coronary plaque classification with intravascular ultrasound radiofrequency data analysis. Circulation 106(17):2200–2206

    Article  PubMed  Google Scholar 

  10. Stone GW, Maehara A, Lansky AJ, de Bruyne B, Cristea E, Mintz GS, Mehran R, McPherson J, Farhat N, Marso SP, Parise H, Templin B, White R, Zhang Z, Serruys PW (2011) A prospective natural-history study of coronary atherosclerosis. N Engl J Med 364(3):226–235

    Article  PubMed  CAS  Google Scholar 

  11. Pundziute G, Schuijf JD, Jukema JW, Decramer I, Sarno G, Vanhoenacker PK, Reiber JH, Schalij MJ, Wijns W, Bax JJ (2008) Head-to-head comparison of coronary plaque evaluation between multislice computed tomography and intravascular ultrasound radiofrequency data analysis. JACC Cardiovasc Interv 1(2):176–182

    Article  PubMed  Google Scholar 

  12. Voros S, Rinehart S, Qian Z, Vazquez G, Anderson H, Murrieta L, Wilmer C, Carlson H, Taylor K, Ballard W, Karmpaliotis D, Kalynych A, Brown C III (2011) 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. JACC Cardiovasc Interv 4(2):198–208

    Article  PubMed  Google Scholar 

  13. Sarno G, Vanhoenacker P, Decramer I, Schuijf JD, Pundziute G, Margolis P, Gupta S, Bax JJ, Wijns W (2008) Characterisation of the “vulnerable” coronary plaque by multi-detector computed tomography: a correlative study with intravascular ultrasound-derived radiofrequency analysis of plaque composition. EuroIntervention 4(3):318–323

    Article  PubMed  Google Scholar 

  14. Boogers MJ, Broersen A, van Velzen JE, de Graaf FR, El-Naggar HM, Kitslaar PH, Dijkstra J, Delgado V, Boersma E, de Roos A, Schuijf JD, Schalij MJ, Reiber JH, Bax JJ, Jukema JW (2012) Automated quantification of coronary plaque with computed tomography: comparison with intravascular ultrasound using a dedicated registration algorithm for fusion-based quantification. Eur Heart J 33(8):1007–1016

    Article  PubMed  Google Scholar 

  15. Yang G, Kitslaar P, Frenay M, Broersen A, Boogers MJ, Bax JJ, Reiber JH, Dijkstra J (2012) Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography. Int J Cardiovasc Imaging 28(4):921–933

    Article  PubMed  Google Scholar 

  16. Brodoefel H, Reimann A, Heuschmid M, Tsiflikas I, Kopp AF, Schroeder S, Claussen CD, Clouse ME, Burgstahler C (2008) Characterization of coronary atherosclerosis by dual-source computed tomography and HU-based color mapping: a pilot study. Eur Radiol 18(11):2466–2474

    Article  PubMed  CAS  Google Scholar 

  17. Dalager MG, Bottcher M, Andersen G, Thygesen J, Pedersen EM, Dejbjerg L, Gotzsche O, Botker HE (2011) Impact of luminal density on plaque classification by CT coronary angiography. Int J Cardiovasc Imaging 27(4):593–600

    Article  PubMed  Google Scholar 

  18. Akram K, Rinehart S, Voros S (2008) Coronary arterial atherosclerotic plaque imaging by contrast-enhanced computed tomography: fantasy or reality? J Nucl Cardiol 15(6):818–829

    PubMed  Google Scholar 

  19. Boogers MJ, Broersen A, van Velzen JE, de Graaf FR, El-Naggar HM, Kitslaar PH, Dijkstra J, Delgado V, Boersma E, de Roos A, Schuijf JD, Schalij MJ, Reiber JH, Bax JJ, Jukema JW (2012) Automated quantification of coronary plaque with computed tomography: comparison with intravascular ultrasound using a dedicated registration algorithm for fusion-based quantification. Eur Heart J 33(8):1007–1016

    Article  PubMed  Google Scholar 

  20. Papadopoulou SL, Garcia-Garcia HM, Rossi A, Girasis C, Dharampal AS, Kitslaar PH, Krestin GP, de Feyter PJ (2012) Reproducibility of computed tomography angiography data analysis using semiautomated plaque quantification software: implications for the design of longitudinal studies. Int J Cardiovasc Imaging. doi:10.1007/s10554-012-0167-5

  21. van Velzen JE, Schuijf JD, de Graaf FR, Boersma E, Pundziute G, Spano F, Boogers MJ, Schalij MJ, Kroft LJ, de Roos A, Jukema JW, van der Wall EE, Bax JJ (2011) Diagnostic performance of non-invasive multidetector computed tomography coronary angiography to detect coronary artery disease using different endpoints: detection of significant stenosis vs. detection of atherosclerosis. Eur Heart J 32(5):637–645

    Article  PubMed  Google Scholar 

  22. Taylor AJ, Cerqueira M, Hodgson JM, Mark D, Min J, O’Gara P, Rubin GD (2010) ACCF/SCCT/ACR/AHA/ASE/ASNC/NASCI/SCAI/SCMR 2010 appropriate use criteria for cardiac computed tomography. A report of the American College of Cardiology Foundation Appropriate Use Criteria Task Force, the Society of Cardiovascular Computed Tomography, the American College of Radiology, the American Heart Association, the American Society of Echocardiography, the American Society of Nuclear Cardiology, the North American Society for Cardiovascular Imaging, the Society for Cardiovascular Angiography and Interventions, and the Society for Cardiovascular Magnetic Resonance. J Cardiovasc Comput Tomogr 4(6):407–433

    PubMed  Google Scholar 

  23. Leber AW, Becker A, Knez A, von Ziegler F, Sirol M, Nikolaou K, Ohnesorge B, Fayad ZA, Becker CR, Reiser M, Steinbeck G, Boekstegers P (2006) 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 47(3):672–677

    Article  PubMed  Google Scholar 

  24. Bruining N, Roelandt JR, Palumbo A, La GL, Cademartiri F, de Feijter PJ, Mollet N, Van Domburg RT, Serruys PW, Hamers R (2007) Reproducible coronary plaque quantification by multislice computed tomography. Catheter Cardiovasc Interv 69(6):857–865

    Article  PubMed  Google Scholar 

  25. Miszalski-Jamka T, Klimeczek P, Banys R, Krupinski M, Nycz K, Bury K, Lada M, Pelberg R, Kereiakes D, Mazur W (2012) The composition and extent of coronary artery plaque detected by multislice computed tomographic angiography provides incremental prognostic value in patients with suspected coronary artery disease. Int J Cardiovasc Imaging 28(3):621–631

    Article  PubMed  Google Scholar 

  26. Pohle K, Achenbach S, Macneill B, Ropers D, Ferencik M, Moselewski F, Hoffmann U, Brady TJ, Jang IK, Daniel WG (2007) Characterization of non-calcified coronary atherosclerotic plaque by multi-detector row CT: comparison to IVUS. Atherosclerosis 190(1):174–180

    Article  PubMed  CAS  Google Scholar 

  27. Choi JH, Min JK, Labounty TM, Lin FY, Mendoza DD, Shin DH, Ariaratnam NS, Koduru S, Granada JF, Gerber TC, Oh JK, Gwon HC, Choe YH (2011) Intracoronary transluminal attenuation gradient in coronary CT angiography for determining coronary artery stenosis. JACC Cardiovasc Imaging 4(11):1149–1157

    Article  PubMed  Google Scholar 

  28. Horiguchi J, Fujioka C, Kiguchi M, Yamamoto H, Shen Y, Kihara Y (2011) In vitro measurement of CT density and estimation of stenosis related to coronary soft plaque at 100 kV and 120 kV on ECG-triggered scan. Eur J Radiol 77(2):294–298

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

Michiel A. de Graaf is supported by the Dutch Technology Foundation STW, grant 10084 and by a research grant from the Interuniversity Cardiology Institute of The Netherlands (ICIN, Utrecht, The Netherlands). Victoria Delgado received consulting fees from Medtronic and St. Jude Medical. The department of Cardiology received research grants from Biotronik, Medtronic, Boston Scientific Corporation, St. Jude Medical, Lantheus Medical Imaging and GE Healthcare. This work was supported by Dutch Technology Foundation STW, Utrecht, The Netherlands, grant 10084. Pieter H Kitslaar and Johan HC Reiber are employees of Medis medical imaging systems bv and have a research appointment at the Leiden University Medical Center.

Conflict of interest

The remaining authors have no conflicts of interest to disclose.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michiel A. de Graaf.

Rights and permissions

Reprints and permissions

About this article

Cite this article

de Graaf, M.A., Broersen, A., Kitslaar, P.H. et al. Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology. Int J Cardiovasc Imaging 29, 1177–1190 (2013). https://doi.org/10.1007/s10554-013-0194-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10554-013-0194-x

Keywords

Navigation