Skip to main content
Log in

A phantom based evaluation of vessel lumen area quantification for coronary CT angiography

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

Abstract

Coronary computed tomography (CT) angiography is a noninvasive method for visualizing coronary artery disease. However, coronary CT angiography is limited in assessment of stenosis severity by the partial volume effect and calcification. Therefore, an accurate method for assessment of stenosis severity is needed. A 10 cm diameter cylindrical Lucite phantom with holes in the range of 0.4–4.5 mm diameter was fitted in a chest phantom. The holes were filled with an iodine solution of 8 mg/mL. To simulate coronary artery disease, different levels of stenosis were created by inserting Lucite rods into the holes with diameter range of 2–4.5 mm. The resulting lumen cross sectional areas ranged from 1.4 to 12.3 mm2. To simulate arterial calcification, calcium hydroxyapatite rods were inserted into the holes with diameter range of 2–4.5 mm. Images of the phantoms were acquired at 100 kVp using a 320-slice CT scanner. A maual and a semi-automated technique based on integrated Hounsfield units was used to calculate vessel cross-sectional area. There was an excellent correlation between the measured and the known cross-sectional area for both normal and stenotic vessels using the manual and the semi-automated techniques. However, the overall measurement error for the manual method was more than twice as compared with the integrated HU technique. Determination of vessel lumen area using the semi-automated integrated Hounsfield unit technique yields more than a factor of two improvement in precision and accuracy as compared to the existing manual technique for vessels with and without stenosis. This technique can also be used to accurately measure arterial cross-sectional area in the presence of coronary calcification.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Achenbach S, Daniel WG (2001) Noninvasive coronary angiography—an acceptable alternative? N Engl J Med 345(26):1909–1910

    Article  CAS  PubMed  Google Scholar 

  2. Budoff MJ et al (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 

  3. Min JK, Shaw LJ, Berman DS (2010) The present state of coronary computed tomography angiography a process in evolution. J Am Coll Cardiol 55(10):957–965

    Article  PubMed  Google Scholar 

  4. Goldstein JA et al (2007) A randomized controlled trial of multi-slice coronary computed tomography for evaluation of acute chest pain. J Am Coll Cardiol 49(8):863–871

    Article  Google Scholar 

  5. Meijboom WB et al (2008) 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 52(8):636–643

    Article  PubMed  Google Scholar 

  6. Nissen SE (2008) Limitations of computed tomography coronary angiography. J Am Coll Cardiol 52(25):2145–2147

    Article  PubMed  Google Scholar 

  7. Marwan M et al (2011) Coronary vessel and luminal area measurement using dual-source computed tomography in comparison with intravascular ultrasound: effect of window settings on measurement accuracy. J Comput Assist Tomogr 35(1):113–118

    Article  PubMed  Google Scholar 

  8. Komatsu S et al (2014) Quantitative analysis of coronary vessels with optimized intracoronary CT number. Plos One 9(1):e85312

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Jiayin Z et al (2015) Quantification of coronary artery stenosis by area stenosis from cardiac CT angiography. Conf Proc IEEE Eng Med Biol Soc 2015:695–698

    Google Scholar 

  10. Kruk M et al (2014) Impact of coronary artery calcium characteristics on accuracy of CT angiography. JACC Cardiovasc Imaging 7(1):49–58

    Article  PubMed  Google Scholar 

  11. Miller JM et al (2008) Diagnostic performance of coronary angiography by 64-row CT. N Engl J Med 359(22):2324–2336

    Article  CAS  PubMed  Google Scholar 

  12. Fuchs A et al (2015) Feasibility of coronary calcium and stent image subtraction using 320-detector row CT angiography. J Cardiovasc Comput Tomogr 9(5):393–398

    Article  PubMed  PubMed Central  Google Scholar 

  13. Huo Y et al (2012) A validated predictive model of coronary fractional flow reserve. J R Soc Interface 9(71):1325–1338

    Article  PubMed  Google Scholar 

  14. Molloi S et al (2017) Accurate quantification of vessel cross-sectional area using CT angiography: a simulation study. Int J Cardiovasc Imaging 33(3):411–419

    Article  PubMed  Google Scholar 

  15. Rasband WS (1997–2005) ImageJ. http://rsb.info.nih.gov/ij/

  16. Leber AW et al (2005) Quantification of obstructive and nonobstructive coronary lesions by 64-slice computed tomography: a comparative study with quantitative coronary angiography and intravascular ultrasound. J Am Coll Cardiol 46(1):147–154

    Article  PubMed  Google Scholar 

  17. Vavere AL et al (2011) Coronary artery stenoses: accuracy of 64-detector row CT angiography in segments with mild, moderate, or severe calcification—a subanalysis of the CORE-64 trial. Radiology 261(1):100–108

    Article  PubMed  PubMed Central  Google Scholar 

  18. Abdulla J et al (2012) Influence of coronary calcification on the diagnostic accuracy of 64-slice computed tomography coronary angiography: a systematic review and meta-analysis. Int J Cardiovasc Imaging 28(4):943–953

    Article  PubMed  Google Scholar 

  19. Kim HJ et al (2010) Patient-specific modeling of blood flow and pressure in human coronary arteries. Ann Biomed Eng 38(10):3195–3209

    Article  CAS  PubMed  Google Scholar 

  20. Koo BK et al (2011) 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 58(19):1989–1997

    Article  PubMed  Google Scholar 

  21. Min JK et al (2012) Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA 308(12):1237–1245

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Ms. Rachel Smith for her help with data analysis. This work was supported in part by a Grant from Canon America Medical System Inc.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sabee Molloi.

Ethics declarations

Conflict of interest

S. Molloi has received grant funding from Canon America Medical System Inc.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Molloi, S., Johnson, T., Lipinski, J. et al. A phantom based evaluation of vessel lumen area quantification for coronary CT angiography. Int J Cardiovasc Imaging 35, 551–557 (2019). https://doi.org/10.1007/s10554-018-1452-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10554-018-1452-8

Keywords

Navigation