Second-generation motion correction algorithm improves diagnostic accuracy of single-beat coronary CT angiography in patients with increased heart rate

  • Junfu Liang
  • Ying Sun
  • Ziqing Ye
  • Yanchun Sun
  • Lei XuEmail author
  • Zhen Zhou
  • Brian Thomsen
  • Jianying Li
  • Zhonghua Sun
  • Zhanming Fan
Computed Tomography



To assess the effect of a second-generation motion correction algorithm on the diagnostic accuracy of coronary computed tomography angiography (CCTA) using a 256-detector row CT in patients with increased heart rates.


Eighty-one consecutive symptomatic cardiac patients with increased heart rates ( 75 beats per min) were enrolled. All patients underwent CCTA and invasive coronary angiography (ICA). CCTA was performed with a 256-detector row CT using prospectively ECG-triggered single-beat protocol. Images were reconstructed using standard (STD) algorithm, first-generation intra-cycle motion correction (MC1) algorithm, and second-generation intra-cycle motion correction (MC2) algorithm. The image quality of coronary artery segments was assessed by two experienced radiologists using a 4-point scale (1: non-diagnostic and 4: excellent), according to the 18-segment model. Diagnostic performance for segments with significant lumen stenosis (≥ 50%) was compared between STD, MC1, and MC2 by using ICA as the reference standard.


The mean effective dose of CCTA was 1.0 mSv. On per-segment level, the overall image quality score and interpretability were improved to 3.56 ± 0.63 and 99.2% due to the use of MC2, as compared to 2.81 ± 0.85 and 92.5% with STD and 3.21 ± 0.79 and 97.2% with MC1. On per-segment level, compared to STD and MC1, MC2 improved the sensitivity (92.2% vs. 79.2%, 80.7%), specificity (97.8% vs. 82.1%, 90.8%), positive predictive value (89.9% vs. 48.4%, 65.1%), negative predictive value (98.3% vs. 94.9%, 95.7%), and diagnostic accuracy (96.8% vs. 81.5%, 89.0%).


A second-generation intra-cycle motion correction algorithm for single-beat CCTA significantly improves image quality and diagnostic accuracy in patients with increased heart rate.

Key Points

A second-generation motion correction (MC2) algorithm can further improve the image quality of all coronary arteries than a first-generation motion correction (MC1).

MC2 algorithm can significantly reduce the number of false positive segments compared to standard and MC1 algorithm.


Coronary vessels Tomography, X-ray computed Heart rate Motion Coronary angiography 



Area under curve


Beats per minute


Coronary artery disease


Coronary computed tomography angiography


Heart rate variability


Invasive coronary angiography


Left anterior descending artery


Left circumflex artery


First-generation motion correction


Second-generation motion correction


Negative predictive value


Positive predictive value


Right coronary artery





This study has received funding by the National Key R&D Program of China (2016YFC1300300) and The National Natural Science Foundation of China (81641069).

Compliance with ethical standards


The scientific guarantor of this publication is Lei Xu.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: GE Healthcare.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

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

Ethical approval

Institutional Review Board approval was obtained.


• prospective

• diagnostic study

• performed at one institution


  1. 1.
    Andreini D, Pontone G, Mushtaq S et al (2018) Image quality and radiation dose of coronary CT angiography performed with whole-heart coverage CT scanner with intra-cycle motion correction algorithm in patients with atrial fibrillation. Eur Radiol 28:1383–1392CrossRefGoogle Scholar
  2. 2.
    Zhang LJ, Wu SY, Wang J, al e (2010) Diagnostic accuracy of dual-source CT coronary angiography: the effect of average heart rate, heart rate variability, and calcium score in a clinical perspective. Acta Radiol 51:727–740CrossRefGoogle Scholar
  3. 3.
    Leipsic J, Abbara S, Achenbach S et al (2014) SCCT guidelines for the interpretation and reporting of coronary CT angiography: a report of the Society of Cardiovascular Computed Tomography Guidelines Committee. J Cardiovasc Comput Tomogr 8:342–358CrossRefGoogle Scholar
  4. 4.
    Johnson PT, Eng J, Pannu HK, Fishman EK (2008) 64-MDCT angiography of the coronary arteries: nationwide survey of patient preparation practice. AJR Am J Roentgenol 190:743–747CrossRefGoogle Scholar
  5. 5.
    Maurer MH, Hamm B, Dewey M (2009) Survey regarding the clinical practice of cardiac CT in Germany: indications, scanning technique and reporting. Rofo 181:1135–1143CrossRefGoogle Scholar
  6. 6.
    Cademartiri F, Garot J, Tendera M, Zamorano JL (2015) Intravenous ivabradine for control of heart rate during coronary CT angiography: a randomized, double-blind, placebo-controlled trial. J Cardiovasc Comput Tomogr 9:286–294CrossRefGoogle Scholar
  7. 7.
    Aghayev A, Murphy D, Keraliya A, Steigner M (2016) Recent developments in the use of computed tomography scanners in coronary artery imaging. Expert Rev Med Devices 13:545–553CrossRefGoogle Scholar
  8. 8.
    Kalisz K, Buethe J, Saboo SS, Abbara S, Halliburton S, Rajiah P (2016) Artifacts at cardiac CT: physics and solutions. Radiographics 36:2064–2083CrossRefGoogle Scholar
  9. 9.
    Muenel D, Noel PB, Dorn F, Dobritz M, Rummeny EJ, Huber A (2011) Step and shoot coronary CT angiography using 256-slice CT: effect of heart rate and heart rate variability on image quality. Eur Radiol 21:2277–2284CrossRefGoogle Scholar
  10. 10.
    Chen Y, Wei D, Li D et al (2018) The value of 16-cm wide-detector computed tomography in coronary computed tomography angiography for patients with high heart rate variability. J Comput Assist Tomogr 42:906–911CrossRefGoogle Scholar
  11. 11.
    Liang J, Wang H, Xu L et al (2017) Diagnostic performance of 256-row detector coronary CT angiography in patients with high heart rates within a single cardiac cycle: a preliminary study. Clin Radiol 72:694–697CrossRefGoogle Scholar
  12. 12.
    Oda S, Honda K, Yoshimura A et al (2016) 256-slice coronary computed tomographic angiography in patients with atrial fibrillation: optimal reconstruction phase and image quality. Eur Radiol 26:55–63CrossRefGoogle Scholar
  13. 13.
    Latif MA, Sanchez FW, Sayegh K et al (2016) Volumetric single-beat coronary computed tomography angiography: relationship of image quality, heart rate, and body mass index. Initial patient experience with a new computed tomography scanner. J Comput Assist Tomogr 40:763–772Google Scholar
  14. 14.
    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 6:164–171CrossRefGoogle Scholar
  15. 15.
    Soon J, Sulaiman N, Park JK et al (2016) The effect of a whole heart motion-correction algorithm on CT image quality and measurement reproducibility in pre-TAVR aortic annulus evaluation. J Cardiovasc Comput Tomogr 10:386e390CrossRefGoogle Scholar
  16. 16.
    Benz DC, Gräni C, Hirt Moch B et al (2016) Minimized radiation and contrast agent exposure for coronary computed tomography angiography: first clinical experience on a latest generation 256-slice scanner. Acad Radiol 23:1008–1014CrossRefGoogle Scholar
  17. 17.
    Machida H, Lin X, Fukui R et al (2015) Influence of the motion correction algorithm on the quality and interpretability of images of single-source 64-detector coronary CT angiography among patients grouped by heart rate. Jpn J Radiol 33:84–93CrossRefGoogle Scholar
  18. 18.
    Pontone G, Bertella E, Mushtaq S et al (2014) Coronary artery disease: diagnostic accuracy of CT coronary angiography--a comparison of high and standard spatial resolution scanning. Radiology 271:688–694CrossRefGoogle Scholar
  19. 19.
    Deak PD, Smal Y, Kalender WA et al (2010) Multisection CT protocols: sex- and age-specific conversion factors used to determine effective dose from dose-length product. Radiology 257:158–166CrossRefGoogle Scholar
  20. 20.
    Fuchs TA, Stehli J, Dougoud S et al (2014) Impact of a new motion-correction algorithm on image quality of low-dose coronary CT angiography in patients with insufficient heart rate control. Acad Radiol 21:312–317CrossRefGoogle Scholar
  21. 21.
    Lee H, Kim JA, Lee JS, Suh J, Paik SH, Park JS (2014) Impact of a vendor-specific motion-correction algorithm on image quality, interpretability, and diagnostic performance of daily routine coronary CT angiography: influence of heart rate on the effect of motion-correction. Int J Cardiovasc Imaging 30:1603–1612CrossRefGoogle Scholar
  22. 22.
    Fan L, Zhang J, Xu D, Dong Z, Li X, Zhang L (2015) CTCA image quality improvement by using snapshot freeze technique under prospective and retrospective electrocardiographic gating. J Comput Assist Tomogr 39:202–206CrossRefGoogle Scholar
  23. 23.
    Andreini D, Pontone G, Mushtaq S et al (2015) Low-dose CT coronary angiography with a novel IntraCycle motion-correction algorithm in patients with high heart rate or heart rate variability. Eur Heart J Cardiovasc Imaging 16:1093–1100CrossRefGoogle Scholar
  24. 24.
    Liang J, Wang H, Xu L et al (2018) Impact of SSF on diagnostic performance of coronary computed tomography angiography within 1 heart beat in patients with high heart rate using a 256-row detector computed tomography. J Comput Assist Tomogr 42:54–61CrossRefGoogle Scholar
  25. 25.
    Carrascosa P, Deviggiano A, Leipsic JA et al (2015) Dual energy imaging and intracycle motion correction for CT coronary angiography in patients with intermediate to high likelihood of coronary artery disease. Clin Imaging 39:1000–1005CrossRefGoogle Scholar
  26. 26.
    Machida H, Fukui R, Gao J et al (2016) Reduction of coronary motion artifacts in prospectively electrocardiography-gated coronary computed tomography angiography using monochromatic imaging at various energy levels in combination with a motion correction algorithm on single-source fast tube voltage switching dual-energy computed tomography. Invest Radiol 51:513–519CrossRefGoogle Scholar
  27. 27.
    Carrascosa P, Deviggiano A, Capunay C, De Zan MC, Goldsmith A, Rodriguez-Granillo GA (2015) Effect of intracycle motion correction algorithm on image quality and diagnostic performance of computed tomography coronary angiography in patients with suspected coronary artery disease. Acad Radiol 22:81–86CrossRefGoogle Scholar
  28. 28.
    Tang PH, Du BJ, Fang XM, Hu XY, Qian PY, Gao QS (2016) Submillisievert coronary CT angiography with adaptive prospective ECG-triggered sequence acquisition and iterative reconstruction in patients with high heart rate on the dual-source CT. J XRay Sci Technol 24:807–820Google Scholar
  29. 29.
    Suh YJ, Kim YJ, Kim JY et al (2017) A whole-heart motion-correction algorithm: effects on CT image quality and diagnostic accuracy of mechanical valve prosthesis abnormalities. J Cardiovasc Comput Tomogr 11:474–481CrossRefGoogle Scholar
  30. 30.
    Koplay M, Erdogan H, Avci A et al (2016) Radiation dose and diagnostic accuracy of high-pitch dual-source coronary angiography in the evaluation of coronary artery stenoses. Diagn Interv Imaging 97:461–469CrossRefGoogle Scholar
  31. 31.
    Leschka S, Scheffel H, Husmann L et al (2008) Effect of decrease in heart rate variability on the diagnostic accuracy of 64-MDCT coronary angiography. AJR Am J Roentgenol 190:1583–1590CrossRefGoogle Scholar
  32. 32.
    Matt D, Scheffel H, Leschka S et al (2007) Dual-source CT coronary angiography: image quality, mean heart rate, and heart rate variability. AJR Am J Roentgenol 189:567–573CrossRefGoogle Scholar
  33. 33.
    Brodoefel H, Burgstahler C, Tsiflikas I et al (2008) Dual-source CT: effect of heart rate, heart rate variability, and calcification on image quality and diagnostic accuracy. Radiology 247:346–355CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Junfu Liang
    • 1
    • 2
  • Ying Sun
    • 2
  • Ziqing Ye
    • 2
  • Yanchun Sun
    • 2
  • Lei Xu
    • 1
    Email author
  • Zhen Zhou
    • 1
  • Brian Thomsen
    • 3
  • Jianying Li
    • 4
  • Zhonghua Sun
    • 5
  • Zhanming Fan
    • 1
  1. 1.Department of Radiology, Beijing Anzhen HospitalCapital Medical UniversityBeijingChina
  2. 2.Department of RadiologyBeijing Huairou HospitalBeijingChina
  3. 3.GE HealthcareMilwaukeeUSA
  4. 4.CT LaboratoryGE Healthcare ChinaBeijingChina
  5. 5.Department of Medical Radiation SciencesCurtin UniversityPerthAustralia

Personalised recommendations