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Components of carotid atherosclerotic plaque in spectral photon-counting CT with histopathologic comparison

  • Computed Tomography
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A Correction to this article was published on 30 June 2023

This article has been updated

Abstract

Objectives

This study aimed to demonstrate the effectiveness of spectral photon-counting CT (SPCCT) in quantifying fibrous cap (FC) thickness, FC area, and lipid-rich necrotic core (LRNC) area, in excised carotid atherosclerotic plaques by comparing it with histopathological measurements.

Methods

This is a single-center ex vivo cross-sectional observational study. Excised plaques of 20 patients (71 +/- 6 years; 13 men), obtained from carotid endarterectomy were scanned with SPCCT using standardized acquisition settings (120k Vp/19 μA; 7–18 keV, 18–30 keV, 30–45 keV, 45–75 keV, and 75–118 keV). FC thickness, FC area, and LRNC area were quantified and compared between high-resolution 3D multi-energy CT images and histopathology using the Wilcoxon signed-ranks test and Bland–Altman analysis. Images were interpreted twice by two radiologists separately, blinded to the histopathology; inter- and intra-rater reliability were assessed with the intra-class correlation coefficients (ICC).

Results

FC thickness and FC area did not show significant differences between the SPCCT-derived radiological measurements versus the histopathological measurements (p value range 0.15–0.51 for FC thickness and 0.053–0.30 for FC area). For the LRNC area, the p value was statistically non-significant for reader 1 (range 0.36–0.81). The Bland-Altman analysis showed mean difference and 95% confidence interval for FC thickness, FC area, and LRNC area, 0.04 (−0.36 to 0.12) square root mm, −0.18 (−0.34 to −0.02) log10 mm2 and 0.10 (−0.088. to 0.009) log10 mm2 respectively.

Conclusion

The result demonstrated a viable technique for quantifying FC thickness, FC area, and LRNC area due to the combined effect of high spatial and energy resolution of SPCCT.

Key Points

• SPCCT can identify and quantify different components of carotid atherosclerotic plaque in ex vivo study.

• Components of atherosclerotic plaque did not show significant differences between the SPCCT-derived radiological measurements versus the histopathological measurements.

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Abbreviations

HU:

Hounsfield unit

MD:

Material decomposition

SPCCT:

Spectral photon-counting computed tomography

TIA:

Transient ischemic attack

References

  1. World Health Organization (2018) WHO methods and data sources for global burden of disease estimates 2000-2016. Global Health Estimates Technical Paper WHO/HIS/IER/GHE/20184, WHO, Geneva

  2. Ooi YC, Gonzalez NR (2015) Management of extracranial carotid artery disease. Cardiol Clin 33:1–35

    Article  PubMed  PubMed Central  Google Scholar 

  3. Insull W Jr (2009) The pathology of atherosclerosis: plaque development and plaque responses to medical treatment. Am J Med 122:S3–S14

    Article  CAS  PubMed  Google Scholar 

  4. Shah PK (2003) Mechanisms of plaque vulnerability and rupture. J Am Coll Cardiol 41:S15–S22

    Article  Google Scholar 

  5. Mughal MM, Khan MK, DeMarco JK, Majid A, Shamoun F, Abela GS (2011) Symptomatic and asymptomatic carotid artery plaque. Expert Rev Cardiovasc Ther 9:1315–1330

    Article  PubMed  PubMed Central  Google Scholar 

  6. Brinjikji W, Huston J, Rabinstein AA, Kim G-M, Lerman A, Lanzino G (2016) Contemporary carotid imaging: from degree of stenosis to plaque vulnerability. J Neurosurg 124:27–42

    Article  CAS  PubMed  Google Scholar 

  7. Saba L, Saam T, Jäger HR et al (2019) Imaging biomarkers of vulnerable carotid plaques for stroke risk prediction and their potential clinical implications. Lancet Neurol 18:559–572

    Article  PubMed  Google Scholar 

  8. Lin E, Alessio A (2009) What are the basic concepts of temporal, contrast, and spatial resolution in cardiac CT? J Cardiovasc Comput Tomogr 3:403–408

    Article  PubMed  PubMed Central  Google Scholar 

  9. Rothwell PM, Eliasziw M, Gutnikov SA et al (2003) Analysis of pooled data from the randomised controlled trials of endarterectomy for symptomatic carotid stenosis. Lancet 361:107–116

    Article  CAS  PubMed  Google Scholar 

  10. Hellings WE, Peeters W, Moll FL et al (2010) Composition of carotid atherosclerotic plaque is associated with cardiovascular outcome: a prognostic study. Circulation 121:1941–1950

    Article  PubMed  Google Scholar 

  11. Ballabriga R, Alozy J, Campbell M et al (2016) Review of hybrid pixel detector readout ASICs for spectroscopic X-ray imaging. J Instrum 11:P01007

    Article  Google Scholar 

  12. Panta RK, Butler APH, Butler PH et al First human imaging with MARS photon-counting CT2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC). IEEE, pp 1-7

  13. Marfo E, Anderson NG, Butler APH et al (2020) Assessment of material identification errors, image quality and radiation doses using small animal spectral photon-counting CT. IEEE Trans Radiat Plasma Med Sci 5:578–587

    Article  Google Scholar 

  14. Panta RK, Walsh MF, Bell ST, Anderson NG, Butler AP, Butler PH (2014) Energy calibration of the pixels of spectral x-ray detectors. IEEE Trans Med Imaging 34:697–706

    Article  PubMed  Google Scholar 

  15. Butler PH, Adebileje SA, Alexander SD et al MARS pre-clinical imaging: the benefits of small pixels and good energy dataDevelopments in X-Ray Tomography XII. International Society for Optics and Photonics, pp 111130C–111130C

  16. Aamir R, Chernoglazov A, Bateman CJ et al (2014) MARS spectral molecular imaging of lamb tissue: data collection and image analysis. J Instrum 9:P02005–P02005

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Bateman CJ, Knight D, Brandwacht B et al (2018) MARS-MD: rejection based image domain material decomposition. J Instrum 13:P05020

    Article  Google Scholar 

  19. Stary HC, Chandler AB, Dinsmore RE et al (1995) A definition of advanced types of atherosclerotic lesions and a histological classification of atherosclerosis: a report from the Committee on Vascular Lesions of the Council on Arteriosclerosis, American Heart Association. Circulation 92:1355–1374

    Article  CAS  PubMed  Google Scholar 

  20. Grant EG, Benson CB, Moneta GL et al (2003) Carotid artery stenosis: gray-scale and Doppler US diagnosis—Society of Radiologists in Ultrasound Consensus Conference. Radiology 229:340–346

    Article  PubMed  Google Scholar 

  21. Stary HC (2000) Natural history and histological classification of atherosclerotic lesions: an update. Arterioscler Thromb Vasc Biol 20:1177–1178

    Article  CAS  PubMed  Google Scholar 

  22. Zainon R, Ronaldson JP, Janmale T et al (2012) Spectral CT of carotid atherosclerotic plaque: comparison with histology. Eur Radiol 22:2581–2588

    Article  CAS  PubMed  Google Scholar 

  23. Cai J, Hatsukami TS, Ferguson MS et al (2005) In vivo quantitative measurement of intact fibrous cap and lipid-rich necrotic core size in atherosclerotic carotid plaque: comparison of high-resolution, contrast-enhanced magnetic resonance imaging and histology. Circulation 112:3437–3444

    Article  PubMed  Google Scholar 

  24. Den Hartog A, Bovens S, Koning W et al (2013) Current status of clinical magnetic resonance imaging for plaque characterisation in patients with carotid artery stenosis. Eur J Vasc Endovasc Surg 45:7–21

    Article  Google Scholar 

  25. Stephens K (2021) FDA Approves Siemens Healthineers’ Naeotom Alpha CT Scanner. AXIS Imaging News

  26. Koenig T, Hamann E, Procz S et al (2013) Charge summing in spectroscopic x-ray detectors with high-Z sensors. IEEE Trans Nucl Sci 60:4713–4718

    Article  CAS  Google Scholar 

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Acknowledgements

The authors acknowledge the University of Otago, University of Canterbury, European Council for Nuclear Research (CERN).

Funding

This project was funded by the Ministry of Business, Innovation and Employment (MBIE), New Zealand under contract number UOCX1404, by MARS Bioimaging Ltd, and the Ministry of Education through the MedTech CoRE.

Author information

Authors and Affiliations

  1. University of Maryland, Baltimore, USA

    Shishir Dahal

  2. Department of Physics, Khalifa University, Abu Dhabi, UAE

    Aamir Y. Raja

  3. University of Canterbury, Christchurch, New Zealand

    Emily Searle

  4. Department of Clinical Radiology, Christchurch Hospital, Canterbury District Health Board, Christchurch, New Zealand

    Frances E. Colgan, Jonathan S. Crighton & Anthony P. H. Butler

  5. Department of Vascular Surgery, Christchurch Hospital, Canterbury District Health Board, Christchurch, New Zealand

    Justin Roake

  6. Department of Medical Science and Public Health, University of Cagliari, Sardinia, Italy

    Luca Saba

  7. School of Biological Science, University of Canterbury, Christchurch, New Zealand

    Steven Gieseg

  8. Department of Academic Radiology, University of Otago, Christchurch, New Zealand

    Anthony P. H. Butler

Authors
  1. Shishir Dahal
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  2. Aamir Y. Raja
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  3. Emily Searle
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  4. Frances E. Colgan
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Corresponding author

Correspondence to Shishir Dahal.

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Guarantor

Anthony. P.H. Butler, Professor and head of department of Academic Radiology, University of Otago, Christchurch, New Zealand.

Conflict of interest

Anthony. P.H. Butler is a shareholder and director of MARS Bioimaging Ltd. Shishir Dahal, Steven Gieseg and Aamir Y Raja are shareholders of the company. Others have no financial holding in the company, nor any other conflict of interest to disclose.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

This study is a part of my PhD research. Thus, it has been previously reported in my thesis.

Methodology

• prospective

• cross-sectional study

• performed at one institution

Additional information

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Company name

The scanner used in this research was developed by MARS Bioimaging Ltd., Christchurch, New Zealand.

The work originated from the University of Otago, Christchurch, 2 Riccarton Avenue, Christchurch Central, Christchurch 8011, New Zealand.

The original online version of this article was revised: In this article the affiliation details for Author Aamir Y. Raja were incorrectly given as: 2 Department of Academic Radiology, University of Otago, Christchurch, New Zealand 3 Department of Physics, Khalifa University, Abu Dhabi, UAE. The correct affiliation should have been: 3 Department of Physics, Khalifa University, Abu Dhabi, UAE

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Dahal, S., Raja, A.Y., Searle, E. et al. Components of carotid atherosclerotic plaque in spectral photon-counting CT with histopathologic comparison. Eur Radiol 33, 1612–1619 (2023). https://doi.org/10.1007/s00330-022-09155-x

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