European Radiology

, Volume 26, Issue 6, pp 1871–1878 | Cite as

Impact of an advanced image-based monoenergetic reconstruction algorithm on coronary stent visualization using third generation dual-source dual-energy CT: a phantom study

  • Stefanie Mangold
  • Paola M. Cannaó
  • U. Joseph Schoepf
  • Julian L. Wichmann
  • Christian Canstein
  • Stephen R. Fuller
  • Giuseppe Muscogiuri
  • Akos Varga-Szemes
  • Konstantin Nikolaou
  • Carlo N. De Cecco
Computed Tomography



To evaluate the impact of an advanced monoenergetic (ME) reconstruction algorithm on CT coronary stent imaging in a phantom model.

Materials and methods

Three stents with lumen diameters of 2.25, 3.0 and 3.5 mm were examined with a third-generation dual-source dual-energy CT (DECT). Tube potential was set at 90/Sn150 kV for DE and 70, 90 or 120 kV for single-energy (SE) acquisitions and advanced modelled iterative reconstruction was used. Overall, 23 reconstructions were evaluated for each stent including three SE acquisitions and ten advanced and standard ME images with virtual photon energies from 40 to 130 keV, respectively. In-stent luminal diameter was measured and compared to nominal lumen diameter to determine stent lumen visibility. Contrast-to-noise ratio was calculated.


Advanced ME reconstructions substantially increased lumen visibility in comparison to SE for stents ≤3 mm. 130 keV images produced the best mean lumen visibility: 86 % for the 2.25 mm stent (82 % for standard ME and 64 % for SE) and 82 % for the 3.0 mm stent (77 % for standard ME and 69 % for SE). Mean DLP for SE 120 kV and DE acquisitions were 114.4 ± 9.8 and 58.9 ± 2.2 mGy × cm, respectively.


DECT with advanced ME reconstructions improves the in-lumen visibility of small stents in comparison with standard ME and SE imaging.

Key Points

An advanced image-based monoenergetic reconstruction algorithm improves lumen visualization in stents ≤3.0 mm.

Application of high keV reconstructions significantly improves in-stent lumen visualization.

DECT acquisition resulted in 49 % radiation dose reduction compared with 120 kV SE.


Dual-energy CT Coronary CT Stent Monoenergetic imaging Iterative reconstruction 



Coronary computed tomography


Dual-energy computed tomography


kilo-electron volts




Contrast-to-noise ratio






Volume-based computed tomography dose index



The scientific guarantor of this publication is Carlo N. De Cecco. The authors of this manuscript declare relationships with the following companies: Dr. Schoepf is a consultant for and receives research support from Bayer, Bracco, GE, Medrad, and Siemens. Mr. Canstein is a Siemens employee. The authors state that this work has not received any funding. No complex statistical methods were necessary for this paper. Institutional Review Board approval was not required because the study was performed by using a thoracic phantom model. No study subjects or cohorts have been previously reported. Methodology: prospective, experimental, performed at one institution.


  1. 1.
    Mahnken AH (2012) CT imaging of coronary stents: past, present, and future. ISRN Cardiol 139823Google Scholar
  2. 2.
    Min JK, Swaminathan RV, Vass M, Gallagher S, Weinsaft JW (2009) High-definition multidetector computed tomography for evaluation of coronary artery stents: comparison to standard-definition 64-detector row computed tomography. J Cardiovasc Comput Tomogr 3:246–251CrossRefPubMedGoogle Scholar
  3. 3.
    Gassenmaier T, Petri N, Allmendinger T et al (2014) Next generation coronary CT angiography: in vitro evaluation of 27 coronary stents. Eur Radiol 24:2953–2961CrossRefPubMedGoogle Scholar
  4. 4.
    Ulrich A, Burg MC, Raupach R et al (2015) Coronary stent imaging with dual-source CT: assessment of lumen visibility using different convolution kernels and postprocessing filters. Acta Radiol 56:42–50CrossRefPubMedGoogle Scholar
  5. 5.
    von Spiczak J, Morsbach F, Winklhofer S et al (2013) Coronary artery stent imaging with CT using an integrated electronics detector and iterative reconstructions: first in vitro experience. J Cardiovasc Comput Tomogr 7:215–222CrossRefGoogle Scholar
  6. 6.
    Eisentopf J, Achenbach S, Ulzheimer S et al (2013) Low-dose dual-source CT angiography with iterative reconstruction for coronary artery stent evaluation. JACC Cardiovasc Imaging 6:458–465CrossRefPubMedGoogle Scholar
  7. 7.
    Zhou Q, Jiang B, Dong F, Huang P, Liu H, Zhang M (2014) Computed tomography coronary stent imaging with iterative reconstruction: a trade-off study between medium kernel and sharp kernel. J Comput Assist Tomogr 38:604–612CrossRefPubMedGoogle Scholar
  8. 8.
    Geyer LL, Glenn GR, De Cecco CN et al (2015) CT evaluation of small-diameter coronary artery stents: effect of an integrated circuit detector with iterative reconstruction. Radiology 276:706–714Google Scholar
  9. 9.
    Wichmann JL, Arbaciauskaite R, Kerl JM et al (2014) Evaluation of monoenergetic late iodine enhancement dual-energy computed tomography for imaging of chronic myocardial infarction. Eur Radiol 24:1211–1218CrossRefPubMedGoogle Scholar
  10. 10.
    Bamberg F, Dierks A, Nikolaou K, Reiser MF, Becker CR, Johnson TR (2011) Metal artifact reduction by dual energy computed tomography using monoenergetic extrapolation. Eur Radiol 21:1424–1429CrossRefPubMedGoogle Scholar
  11. 11.
    Bongers MN, Schabel C, Krauss B et al (2014) Noise-optimized virtual monoenergetic images and iodine maps for the detection of venous thrombosis in second-generation dual-energy CT (DECT): an ex vivo phantom study. Eur Radiol 25:1655–1664Google Scholar
  12. 12.
    Schabel C, Bongers M, Sedlmair M et al (2014) Assessment of the hepatic veins in poor contrast conditions using dual energy CT: evaluation of a novel monoenergetic extrapolation software algorithm. Röfo 186:591–597PubMedGoogle Scholar
  13. 13.
    Grant KL, Flohr TG, Krauss B, Sedlmair M, Thomas C, Schmidt B (2014) Assessment of an advanced image-based technique to calculate virtual monoenergetic computed tomographic images from a dual-energy examination to improve contrast-to-noise ratio in examinations using iodinated contrast media. Investig Radiol 49:586–592CrossRefGoogle Scholar
  14. 14.
    Stehli J, Fuchs TA, Singer A et al (2014) First experience with single-source, dual-energy CCTA for monochromatic stent imaging. Eur Heart J Cardiovasc Imaging 16:507–512Google Scholar
  15. 15.
    Winklehner A, Goetti R, Baumueller S et al (2011) Automated attenuation-based tube potential selection for thoracoabdominal computed tomography angiography: improved dose effectiveness. Investig Radiol 46:767–773CrossRefGoogle Scholar
  16. 16.
    Gilard M, Cornily JC, Pennec PY et al (2006) Assessment of coronary artery stents by 16 slice computed tomography. Heart 92:58–61CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Carbone I, Francone M, Algeri E et al (2008) Non-invasive evaluation of coronary artery stent patency with retrospectively ECG-gated 64-slice CT angiography. Eur Radiol 18:234–243CrossRefPubMedGoogle Scholar
  18. 18.
    Oncel D, Oncel G, Tastan A, Tamci B (2008) Evaluation of coronary stent patency and in-stent restenosis with dual-source CT coronary angiography without heart rate control. AJR Am J Roentgenol 191:56–63CrossRefPubMedGoogle Scholar
  19. 19.
    Pugliese F, Weustink AC, Van Mieghem C et al (2008) Dual source coronary computed tomography angiography for detecting in-stent restenosis. Heart 94:848–854CrossRefPubMedGoogle Scholar
  20. 20.
    Taylor AJ, Cerqueira M, Hodgson JM et al (2010) ACCF/SCCT/ACR/AHA/ASE/ASNC/NASCI/SCAI/SCMR 2010 appropriate Use criteria for cardiac computed tomography. A report of the AMERICAN college of Cardiology Foundation appropriate use criteria task force, the society of cardiovascular computed tomography, the American College of Radiology, the American Heart Association, the American Society of Echocardiography, the American Society of Nuclear Cardiology, the North American Society for Cardiovascular Imaging, the Society for Cardiovascular Angiography and Interventions, and the Society for Cardiovascular Magnetic Resonance. Circulation 122:e525–e555CrossRefPubMedGoogle Scholar
  21. 21.
    Ebersberger U, Tricarico F, Schoepf UJ et al (2013) CT evaluation of coronary artery stents with iterative image reconstruction: improvements in image quality and potential for radiation dose reduction. Eur Radiol 23:125–132CrossRefPubMedGoogle Scholar
  22. 22.
    Leipsic J, Heilbron BG, Hague C (2012) Iterative reconstruction for coronary CT angiography: finding its way. Int J Cardiovasc Imaging 28:613–620CrossRefPubMedGoogle Scholar
  23. 23.
    Renker M, Geyer LL, Krazinski AW, Silverman JR, Ebersberger U, Schoepf UJ (2013) Iterative image reconstruction: a realistic dose-saving method in cardiac CT imaging? Expert Rev Cardiovasc Ther 11:403–409CrossRefPubMedGoogle Scholar
  24. 24.
    Maintz D, Burg MC, Seifarth H et al (2009) Update on multidetector coronary CT angiography of coronary stents: in vitro evaluation of 29 different stent types with dual-source CT. Eur Radiol 19:42–49CrossRefPubMedGoogle Scholar
  25. 25.
    Maintz D, Seifarth H, Raupach R et al (2006) 64-slice multidetector coronary CT angiography: in vitro evaluation of 68 different stents. Eur Radiol 16:818–826CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2015

Authors and Affiliations

  • Stefanie Mangold
    • 1
    • 2
  • Paola M. Cannaó
    • 1
    • 3
  • U. Joseph Schoepf
    • 1
    • 4
  • Julian L. Wichmann
    • 1
    • 5
  • Christian Canstein
    • 6
  • Stephen R. Fuller
    • 1
  • Giuseppe Muscogiuri
    • 1
    • 7
  • Akos Varga-Szemes
    • 1
  • Konstantin Nikolaou
    • 2
  • Carlo N. De Cecco
    • 1
    • 7
  1. 1.Division of Cardiovascular Imaging, Department of Radiology and Radiological ScienceMedical University of South CarolinaCharlestonUSA
  2. 2.Department of Diagnostic and Interventional RadiologyEberhard-Karls University TuebingenTuebingenGermany
  3. 3.Scuola di Specializzazione in RadiodiagnosticaUniversity of MilanMilanItaly
  4. 4.Division of Cardiology, Department of MedicineMedical University of South CarolinaCharlestonUSA
  5. 5.Department of Diagnostic and Interventional RadiologyUniversity Hospital FrankfurtFrankfurtGermany
  6. 6.Siemens Medical SolutionsMalvernUSA
  7. 7.Department of Radiological Sciences, Oncology and PathologyUniversity of Rome “Sapienza”RomeItaly

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