The International Journal of Cardiovascular Imaging

, Volume 34, Issue 12, pp 1977–1985 | Cite as

The effect of heart rate on coronary plaque measurements in 320-row coronary CT angiography

  • Masafumi KidohEmail author
  • Daisuke Utsunomiya
  • Yoshinori Funama
  • Daisuke Sakabe
  • Seitaro Oda
  • Takeshi Nakaura
  • Hideaki Yuki
  • Yasunori Nagayama
  • Kenichiro Hirata
  • Yuji Iyama
  • Tomohiro Namimoto
  • Yasuyuki Yamashita
Original Paper


Repeatability of quantitative assessment of atherosclerotic plaques is important for the accurate detection of high-risk plaques in coronary CT angiography (CTA). We assessed the effect of heart rate (HR) on plaque CT number using a coronary artery model and a cardiac phantom capable of simulating cardiac motion. The coronary artery model with luminal stenosis on a cardiac phantom was imaged with a simulated HR of 0, 50, 60, and 70 beats per minute using a 320-row CT scanner. We reconstructed CT images for cardiac diastolic phases (for 75% R–R interval) using filtered back projection (FBP), hybrid iterative reconstruction (AIDR3D), and model-based iterative reconstruction (FIRST). Two observers measured plaque attenuation in the lesion with 75% stenosis. The coefficient of determination (R2) was obtained to evaluate interobserver agreement. At HR 70, FIRST improved the correlation between two observers compared with FBP and AIDR3D (FIRST: R2 = 0.68, p < 0.05; FBP: R2 = 0.29, p = 0.31; AIDR3D: R2 = 0.22, p = 0.18). These R2 at HR 70 were lower compared with at HR 50 (FIRST: R2 = 0.92, p < 0.05; FBP: R2 = 0.83, p < 0.05; AIDR3D: R2 = 0.87, p < 0.05) and HR 0 (FIRST: R2 = 0.97, p < 0.05; FBP: R2 = 0.89, p < 0.05; AIDR3D: R2 = 0.95, p < 0.05). Higher HR affected plaque measurement repeatability in coronary CTA. FIRST may improve plaque measurement repeatability at the higher HR compared with FBP and AIDR3D.


Coronary computed tomography angiography Coronary artery disease Coronary artery plaque Non-calcified plaque Model-based iterative reconstruction 



Computed tomography angiography


Hounsfield unit


Intravascular ultrasound


Heart rate


Beats per minute


Filtered back projection


Adaptive iterative dose reduction 3D


Forward projected model-based iterative reconstruction solution


Region of interest


Standard deviation


Intraclass correlation coefficients


Limits of agreement


Coronary Artery Disease Reporting and Data System


Mean difference




Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

Supplementary material 1 (MOV 6650 KB)


  1. 1.
    Miller JM, Rochitte CE, Dewey M et al (2008) Diagnostic performance of coronary angiography by 64-row CT. N Engl J Med 359(22):2324–2336CrossRefGoogle Scholar
  2. 2.
    Gu H, Gao Y, Hou Z et al (2017) Prognostic value of coronary atherosclerosis progression evaluated by coronary CT angiography in patients with stable angina. Eur Radiol 28:1066–1076Google Scholar
  3. 3.
    Ghoshhajra BB, Takx RAP, Staziaki PV et al (2017) Clinical implementation of an emergency department coronary computed tomographic angiography protocol for triage of patients with suspected acute coronary syndrome. Eur Radiol 27(7):2784–2793CrossRefGoogle Scholar
  4. 4.
    Ferencik M, Mayrhofer T, Puchner SB et al (2015) Computed tomography-based high-risk coronary plaque score to predict acute coronary syndrome among patients with acute chest pain—results from the ROMICAT II trial. J Cardiovasc Comput Tomogr 9(6):538–545CrossRefGoogle Scholar
  5. 5.
    Cury RC, Abbara S, Achenbach S et al (2016) CAD-RADS (TM) Coronary artery disease—reporting and data system. An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Radiology (ACR) and the North American Society for Cardiovascular Imaging (NASCI). Endorsed by the American College of Cardiology. J Cardiovasc Comput Tomogr 10(4):269–281CrossRefGoogle Scholar
  6. 6.
    Tarkin JM, Dweck MR, Evans NR et al (2016) Imaging atherosclerosis. Circ Res 118(4):750–769CrossRefGoogle Scholar
  7. 7.
    Motoyama S, Sarai M, Harigaya H et al (2009) Computed tomographic angiography characteristics of atherosclerotic plaques subsequently resulting in acute coronary syndrome. J Am Coll Cardiol 54(1):49–57CrossRefGoogle Scholar
  8. 8.
    Szilveszter B, Celeng C, Maurovich-Horvat P (2016) Plaque assessment by coronary CT. Int J Cardiovasc Imaging 32(1):161–172CrossRefGoogle Scholar
  9. 9.
    Shaw LJ, Narula J, Chandrashekhar Y (2015) The never-ending story on coronary calcium: is it predictive, punitive, or protective? J Am Coll Cardiol 65(13):1283–1285CrossRefGoogle Scholar
  10. 10.
    Kitagawa T, Yamamoto H, Horiguchi J et al (2011) Effects of statin therapy on non-calcified coronary plaque assessed by 64-slice computed tomography. Int J Cardiol 150(2):146–150CrossRefGoogle Scholar
  11. 11.
    Auscher S, Heinsen L, Nieman K et al (2015) Effects of intensive lipid-lowering therapy on coronary plaques composition in patients with acute myocardial infarction: assessment with serial coronary CT angiography. Atherosclerosis 241(2):579–587CrossRefGoogle Scholar
  12. 12.
    Cademartiri F, Mollet NR, Runza G et al (2005) 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 15(7):1426–1431CrossRefGoogle Scholar
  13. 13.
    Achenbach S, Boehmer K, Pflederer T et al (2010) Influence of slice thickness and reconstruction kernel on the computed tomographic attenuation of coronary atherosclerotic plaque. J Cardiovasc Comput Tomogr 4(2):110–115CrossRefGoogle Scholar
  14. 14.
    Suzuki S, Furui S, Kuwahara S et al (2006) Accuracy of attenuation measurement of vascular wall in vitro on computed tomography angiography: effect of wall thickness, density of contrast medium, and measurement point. Invest Radiol 41(6):510–515CrossRefGoogle Scholar
  15. 15.
    Kidoh M, Utsunomiya D, Oda S et al (2016) Evaluation of the effect of intracoronary attenuation on coronary plaque measurements using a dual-phase coronary CT angiography technique on a 320-row CT scanner—in vivo validation study. Acad Radiol 23(3):315–320CrossRefGoogle Scholar
  16. 16.
    Tanami Y, Ikeda E, Jinzaki M et al (2010) Computed tomographic attenuation value of coronary atherosclerotic plaques with different tube voltage: an ex vivo study. J Comput Assist Tomogr 34(1):58–63CrossRefGoogle Scholar
  17. 17.
    Dalager MG, Bottcher M, Dalager S et al (2011) Imaging atherosclerotic plaques by cardiac computed tomography in vitro: impact of contrast type and acquisition protocol. Invest Radiol 46(12):790–795CrossRefGoogle Scholar
  18. 18.
    Arbab-Zadeh A, Texter J, Ostbye KM et al (2010) Quantification of lumen stenoses with known dimensions by conventional angiography and computed tomography: implications of using conventional angiography as gold standard. Heart 96(17):1358–1363CrossRefGoogle Scholar
  19. 19.
    Seifarth H, Wienbeck S, Pusken M et al (2007) Optimal systolic and diastolic reconstruction windows for coronary CT angiography using dual-source CT. AJR Am J Roentgenol 189(6):1317–1323CrossRefGoogle Scholar
  20. 20.
    Araoz PA, Kirsch J, Primak AN et al (2009) Optimal image reconstruction phase at low and high heart rates in dual-source CT coronary angiography. Int J Cardiovasc Imaging 25(8):837–845CrossRefGoogle Scholar
  21. 21.
    Funama Y, Utsunomiya D, Hirata K et al (2017) Improved estimation of coronary plaque and luminal attenuation using a vendor-specific model-based iterative reconstruction algorithm in contrast-enhanced ct coronary angiography. Acad Radiol 24(9):1070–1078CrossRefGoogle Scholar
  22. 22.
    Tatsugami F, Higaki T, Sakane H et al (2017) Coronary artery stent evaluation with model-based iterative reconstruction at coronary CT angiography. Acad Radiol 24(8):975–981CrossRefGoogle Scholar
  23. 23.
    Dalager MG, Bottcher M, Andersen G et al (2011) Impact of luminal density on plaque classification by CT coronary angiography. Int J Cardiovasc Imaging 27(4):593–600CrossRefGoogle Scholar
  24. 24.
    Linsen PV, Coenen A, Lubbers MM, Dijkshoorn ML, Ouhlous M, Nieman K (2016) Computed tomography angiography with a 192-slice dual-source computed tomography system: improvements in image quality and radiation dose. J Clin Imaging Sci 6:44CrossRefGoogle Scholar
  25. 25.
    Leipsic J, Labounty TM, Hague CJ et al (2012) Effect of a novel vendor-specific motion-correction algorithm on image quality and diagnostic accuracy in persons undergoing coronary CT angiography without rate-control medications. J Cardiovasc Comput Tomogr 6(3):164–171CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Masafumi Kidoh
    • 1
    Email author
  • Daisuke Utsunomiya
    • 1
  • Yoshinori Funama
    • 2
  • Daisuke Sakabe
    • 1
  • Seitaro Oda
    • 1
  • Takeshi Nakaura
    • 1
  • Hideaki Yuki
    • 1
  • Yasunori Nagayama
    • 1
  • Kenichiro Hirata
    • 1
  • Yuji Iyama
    • 1
  • Tomohiro Namimoto
    • 1
  • Yasuyuki Yamashita
    • 1
  1. 1.Department of Diagnostic Radiology, Faculty of Life SciencesKumamoto UniversityKumamotoJapan
  2. 2.Department of Medical Physics, Faculty of Life SciencesKumamoto UniversityKumamotoJapan

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