Skip to main content
SpringerLink
Log in

Characterization of arterial plaque composition with dual energy computed tomography: a simulation study

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

Abstract

To investigate the feasibility of quantifying the chemical composition of coronary artery plaque in terms of water, lipid, protein, and calcium contents using dual-energy computed tomography (CT) in a simulation study. A CT simulation package was developed based on physical parameters of a clinical CT scanner. A digital thorax phantom was designed to simulate coronary arterial plaques in the range of 2–5 mm in diameter. Both non-calcified and calcified plaques were studied. The non-calcified plaques were simulated as a mixture of water, lipid, and protein, while the calcified plaques also contained calcium. The water, lipid, protein, and calcium compositions of the plaques were selected to be within the expected clinical range. A total of 95 plaques for each lesion size were simulated using the CT simulation package at 80 and 135 kVp. Half-value layer measurements were made to make sure the simulated dose was within the range of clinical dual energy scanning protocols. Dual-energy material decomposition using a previously developed technique was performed to determine the volumetric fraction of water, lipid, protein, and calcium contents in each plaque. For non-calcified plaque, the total volume conservation provides the third constrain for three-material decomposition with dual energy CT. For calcified plaque, a fourth criterion was introduced from a previous report suggesting a linear correlation between water and protein contents in soft tissue. For non-calcified plaque, the root mean-squared error (RMSE) of the image-based decomposition was estimated to be 0.7%, 1.5%, and 0.3% for water, lipid, and protein contents, respectively. As for the calcified plaques, the RMSE of the 5 mm plaques were estimated to be 5.6%, 5.7%, 0.2%, and 3.1%, for water, lipid, calcium, and protein contents, respectively. The RMSE increases as the plaque size reduces. The simulation results indicate that chemical composition of coronary arterial plaques can be quantified using dual-energy CT. By accurately quantifying the content of a coronary plaque lesion, our decomposition method may provide valuable insight for the assessment and stratification of coronary artery disease.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Ren L, Rajendran K, McCollough CH, Yu L (2020) Quantitative accuracy and dose efficiency of dual-contrast imaging using dual-energy CT: a phantom study. Med Phys 47:441–456

    Article  Google Scholar 

  2. McCollough CH, Boedeker K, Cody D et al (2020) Principles and applications of multienergy CT: report of AAPM Task Group 291. Med Phys 47:e881–e912

    Article  CAS  Google Scholar 

  3. Gronberg F, Lundberg J, Sjolin M et al (2020) Feasibility of unconstrained three-material decomposition: imaging an excised human heart using a prototype silicon photon-counting CT detector. Eur Radiol. https://doi.org/10.1007/s00330-020-07017-y

    Article  PubMed  PubMed Central  Google Scholar 

  4. Zhao W, Vernekohl D, Han F et al (2018) A unified material decomposition framework for quantitative dual- and triple-energy CT imaging. Med Phys 45:2964–2977

    Article  Google Scholar 

  5. Raja AY, Moghiseh M, Bateman CJ et al (2018) Measuring identification and quantification errors in spectral CT material decomposition. Appl Sci-Basel 8:467

    Article  Google Scholar 

  6. De Santis D, Eid M, De Cecco CN et al (2018) Dual-energy computed tomography in cardiothoracic vascular imaging. Radiol Clin N Am 56:521

    Article  Google Scholar 

  7. Machida H, Tanaka I, Fukui R et al (2016) Dual-energy spectral CT: various clinical vascular applications. Radiographics 36:1215–1232

    Article  Google Scholar 

  8. Alvarez RE, Macovski A (1976) Energy-selective reconstructions in X-ray computerized tomography. Phys Med Biol 21:733–744

    Article  CAS  Google Scholar 

  9. Lehmann LA, Alvarez RE, Macovski A et al (1981) Generalized image combinations in dual KVP digital radiography. Med Phys 8:659–667

    Article  CAS  Google Scholar 

  10. Tran DN, Straka M, Roos JE et al (2009) Dual-energy CT discrimination of iodine and calcium: experimental results and implications for lower extremity CT angiography. Acad Radiol 16:160–171

    Article  Google Scholar 

  11. Gupta R, Phan CM, Leidecker C et al (2010) Evaluation of dual-energy CT for differentiating intracerebral hemorrhage from iodinated contrast material staining. Radiology 257:205–211

    Article  Google Scholar 

  12. Hubbell JH, Seltzer SM (1995) Tables of x-ray mass attenuation coeeficient and mass energy absorption coefficients 1 keV to 20 MeV for elements Z=1 to 92 and 48 additional substances of dosimetric interest. NIST Report No NISTIR 5632

  13. Liu X, Yu LF, Primak AN, McCollough CH (2009) Quantitative imaging of element composition and mass fraction using dual-energy CT: three-material decomposition. Med Phys 36:1602–1609

    Article  Google Scholar 

  14. Johnson TRC, Krauss B, Sedlmair M et al (2007) Material differentiation by dual energy CT: initial experience. Eur Radiol 17:1510–1517

    Article  Google Scholar 

  15. Obaid DR, Calvert PA, Gopalan D et al (2014) Dual-energy computed tomography imaging to determine atherosclerotic plaque composition: a prospective study with tissue validation. J Cardiovasc Comput 8:230–237

    Article  Google Scholar 

  16. Mandal SR, Bharati A, Haghighi RR et al (2018) Non-invasive characterization of coronary artery atherosclerotic plaque using dual energy CT: explanation in ex-vivo samples. Physica Medica-Eur J Med Phys 45:52–58

    Article  Google Scholar 

  17. Sangiorgi G, Rumberger JA, Severson A et al (1998) Arterial calcification and not lumen stenosis is highly correlated with atherosclerotic plaque burden in humans: a histologic study of 723 coronary artery segments using nondecalcifying methodology. J Am Coll Cardiol 31:126–133

    Article  CAS  Google Scholar 

  18. Kuhnigk JM, Dicken V, Bornemann L et al (2006) Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Trans Med Imaging 25:417–434

    Article  Google Scholar 

  19. Schaar JA, Muller JE, Falk E et al (2003) Terminology for high-risk and vulnerable coronary artery plaques. Report of a meeting on the vulnerable plaque, June 17 and 18, 2003, Santorini, Greece. Eur Heart J 2004(25):1077–1082

    Google Scholar 

  20. Arbab-Zadeh A, Fuster V (2015) The myth of the "vulnerable plaque": transitioning from a focus on individual lesions to atherosclerotic disease burden for coronary artery disease risk assessment. J Am Coll Cardiol 65:846–855

    Article  Google Scholar 

  21. Johri AM, Nambi V, Naqvi TZ et al (2020) Recommendations for the assessment of carotid arterial plaque by ultrasound for the characterization of atherosclerosis and evaluation of cardiovascular risk: from the American Society of Echocardiography. J Am Soc Echocardiogr 33:917–933

    Article  Google Scholar 

  22. Ding H, Ducote JL, Molloi S (2012) Breast composition measurement with a cadmium-zinc-telluride based spectral computed tomography system. Med Phys 39:1289–1297

    Article  CAS  Google Scholar 

  23. Li Z, Leng S, Yu L et al (2015) Image-based material decomposition with a general volume constraint for photon-counting CT. Proc SPIE Int Soc Opt Eng 9412:94120T

    PubMed  PubMed Central  Google Scholar 

  24. Lee SW, Choi YN, Kim HJ (2014) Quantitative material decomposition using spectral computed tomography with an energy-resolved photon-counting detector. Phys Med Biol 59:5457–5482

    Article  Google Scholar 

  25. Badea CT, Holbrook M, Clark DP, Ghaghada K (2018) Spectral imaging of iodine and gadolinium nanoparticles using dual-energy CT. Proceedings Volume 10573, Medical Imaging 2018: Physics of Medical Imaging; 105731I (2018)

  26. Goodsitt MM, Rosenthal DI (1987) Quantitative computed-tomography scanning for measurement of bone and bone-marrow fat-content—a comparison of single-energy and dual-energy techniques using a solid synthetic phantom. Invest Radiol 22:799–810

    Article  CAS  Google Scholar 

  27. Nowotny R, Hofer A (1985) Program for calculating diagnostic x-ray spectra. Rofo 142:685–689

    Article  CAS  Google Scholar 

  28. van Aarle W, Palenstijn WJ, De Beenhouwer J et al (2015) The ASTRA toolbox: a platform for advanced algorithm development in electron tomography. Ultramicroscopy 157:35–47

    Article  Google Scholar 

  29. Johnson T, Ding H, Molloi S (2013) Breast density quantification with breast computed tomography (CT): a post-mortem study. Phys Med Biol 58:8573–8591

    Article  Google Scholar 

  30. Achenbach S, Moselewski F, Ropers D et al (2004) Detection of calcified and noncalcified coronary atherosclerotic plaque by contrast-enhanced, submillimeter multidetector spiral computed tomography—a segment-based comparison with intravascular ultrasound. Circulation 109:14–17

    Article  Google Scholar 

  31. Hoffmann U, Moselewski F, Nieman K et al (2006) Noninvasive assessment of plaque morphology and composition in culprit and stable lesions in acute coronary syndrome and stable lesions in stable angina by multidetector computed tomography. J Am Coll Cardiol 47:1655–1662

    Article  Google Scholar 

  32. Leber AW, Knez A, White CW et al (2003) Composition of coronary atherosclerotic plaques in patients with acute myocardial infarction and stable angina pectoris determined by contrast-enhanced multislice computed tomography. Am J Cardiol 91:714–718

    Article  Google Scholar 

  33. Earls JP, Berman EL, Urban BA et al (2008) Prospectively gated transverse coronary CT angiography versus retrospectively gated helical technique: improved image quality and reduced radiation dose. Radiology 246:742–753

    Article  Google Scholar 

  34. Raff GL, Gallagher MJ, O'Neill W, Goldstein JA (2005) Diagnostic accuracy of noninvasive coronary angiography using 64-slice spiral computed tomography. J Am Coll Cardiol 46:552–557

    Article  Google Scholar 

  35. Ding H, Klopfer MJ, Ducote JL et al (2014) Breast tissue characterization with photon-counting spectral CT imaging: a postmortem breast study. Radiology 272:731–738

    Article  Google Scholar 

  36. Vinnakota KC, Bassingthwaighte JB (2004) Myocardial density and composition: a basis for calculating intracellular metabolite concentrations. Am J Physiol-Heart C 286:H1742–H1749

    Article  CAS  Google Scholar 

  37. Woodard HQ, White DR (1986) The composition of body-tissues. Br J Radiol 59:1209–1219

    Article  CAS  Google Scholar 

  38. McCollough CH, Zink FE, Welch TJ (1999) Performance evaluation of a multi-slice CT system. Radiology 210:586

    Google Scholar 

  39. Kopp AF, Ohnesorge B, Becker C et al (2002) Reproducibility and accuracy of coronary calcium measurements with multi-detector row versus electron-beam CT. Radiology 225:113–119

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Mr. Logan Hubbard for the useful discussion. This work was supported in part by NIH/NCI Grant No. R01CA13687.

Author information

Authors and Affiliations

  1. Department of Radiological Sciences, University of California, Irvine, CA, 92697, USA

    Huanjun Ding, Chenggong Wang, Shant Malkasian, Travis Johnson & Sabee Molloi

Authors
  1. Huanjun Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chenggong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shant Malkasian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Travis Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sabee Molloi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huanjun Ding.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ding, H., Wang, C., Malkasian, S. et al. Characterization of arterial plaque composition with dual energy computed tomography: a simulation study. Int J Cardiovasc Imaging 37, 331–341 (2021). https://doi.org/10.1007/s10554-020-01961-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10554-020-01961-y

Keywords

Search

Navigation