Temporal averaging for analysis of four-dimensional whole-heart computed tomography perfusion of the myocardium: proof-of-concept study

  • S. Feger
  • A. Shaban
  • S. Lukas
  • C. Kendziorra
  • M. Rief
  • E. Zimmermann
  • M. Dewey
Original Paper


To assess the feasibility of four-dimensional (4D) whole-heart computed tomography perfusion (CTP) of the myocardium and the added value of temporal averaging of consecutive 3D datasets from different heartbeats for analysis. We included 30 patients with suspected or known coronary artery disease (CAD) who underwent 320-row coronary CT angiography (CTA) and myocardial CTP. Out of these, 15 patients underwent magnetic resonance myocardial perfusion imaging (MR MPI). All CTP examinations were initiated after 3 min of intravenous infusion of adenosine (140 µg/kg/min) and were performed dynamically covering the entire heart every heart beat over a period of 20 ± 3 heart beats. Temporal averaging for dynamic CTP visualisation was analysed for the combination of two, three, four, six, and eight consecutive 3D datasets. Input time attenuation curves (TAC) were delivered from measurement points in the centre of the left ventricle. In all 30 patients, myocardial 4D CTP was feasible and temporal averaging was successfully implemented for all planned combinations of 3D datasets. Temporal averaging of three consecutive 3D datasets showed best performance in the analysis of all CTP image quality parameters: noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), subjective image quality, and diagnostic accuracy with an improvement of SNR and CNR by a factor of 2.2 ± 1.3 and 1.3 ± 0.9. With increasing level of temporal averaging, the input TACs became smoother, but also shorter. Out of the 11 perfusion defects detected with MR MPI, 9 defects were also visible on the 4D CTP images. Whole-heart CTP of the myocardium is feasible and temporal averaging of dynamic datasets improves quantitative image quality parameters and visualization of perfusion defects while further studies are needed to assess its added value for quantification of perfusion parameters.


Computed tomography Perfusion Analysis Myocardium Temporal averaging 



Body mass index


Beats per minute


Contrast-to-noise ratio


Computed tomography


Hounsfield unit




Left ventricle










Myocardial perfusion imaging


Multiplanar reconstruction


Magnetic resonce




Region of interest




Signal-to-noise ratio


Time attenuation curves


Compliance with ethical standards

Conflict of interest

Prof. Dewey has received grant support from the Heisenberg Program of the DFG for a professorship (DE 1361/14-1), the FP7 Program of the European Commission for the randomized multicenter DISCHARGE trial (603266-2, HEALTH-2012.2.4.-2), the European Regional Development Fund (20072013 2/05, 20072013 2/48), the German Heart Foundation/German Foundation of Heart Research (F/23/08, F/27/10), the Joint Program from the German Research Foundation (DFG) and the German Federal Ministry of Education and Research (BMBF) for meta-analyses (01KG1013, 01KG1110, 01KG1210), GE Healthcare, Bracco, Guerbet, and Toshiba Medical Systems. Prof. Dewey has received lecture fees from Toshiba Medical Systems, Guerbet, Cardiac MR Academy Berlin, and Bayer (Schering-Berlex). Prof. Dewey is a consultant to Guerbet and one of the principal investigators of multi-center studies (CORE-64 and 320) on coronary CT angiography sponsored by Toshiba Medical Systems. He is also the editor of Coronary CT Angiography and Cardiac CT, both published by Springer, and offers hands-on workshops on cardiovascular imaging ( Institutional master research agreements exist with Siemens Medical Solutions, Philips Medical Systems, and Toshiba Medical Systems. The terms of these arrangements are managed by the legal department of Charité—Universitätsmedizin Berlin. All other authors have nothing to disclose.

Ethical approval

All human studies have been approved by the ethics committee and have therefore been performed in accordance with the ethical standards lid down in the 1964 Declaration of Helsinki and its later amendments. All persons gave written informed consent prior to their inclusion in the study.


  1. 1.
    Ko SM, Hwang HK, Kim SM, Cho IH (2015) Multi-modality imaging for the assessment of myocardial perfusion with emphasis on stress perfusion CT and MR imaging. Int J Cardiovasc Imaging 31(Suppl 1):1–21. doi: 10.1007/s10554-015-0645-7 CrossRefPubMedGoogle Scholar
  2. 2.
    Hurlock GS, Higashino H, Mochizuki T (2009) History of cardiac computed tomography: single to 320-detector row multislice computed tomography. Int J Cardiovasc Imaging 25 Suppl 1:31–42. doi: 10.1007/s10554-008-9408-z CrossRefPubMedGoogle Scholar
  3. 3.
    Stanford W (2005) Advances in cardiovascular CT imaging: CT clinical imaging. Int J Cardiovasc Imaging 21(1):29–37CrossRefPubMedGoogle Scholar
  4. 4.
    George RT, Mehra VC, Chen MY, Kitagawa K, Arbab-Zadeh A, Miller JM et al (2015) Myocardial CT perfusion imaging and SPECT for the diagnosis of coronary artery disease: a head-to-head comparison from the CORE320 multicenter diagnostic performance study. Radiology 274(2):626. doi: 10.1148/radiol.14144050 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Rief M, Zimmermann E, Stenzel F, Martus P, Stangl K, Greupner J et al (2013) Computed tomography angiography and myocardial computed tomography perfusion in patients with coronary stents: prospective intraindividual comparison with conventional coronary angiography. J Am Coll Cardiol 62(16):1476–1485. doi: 10.1016/j.jacc.2013.03.088 CrossRefPubMedGoogle Scholar
  6. 6.
    Rocha-Filho JA, Blankstein R, Shturman LD, Bezerra HG, Okada DR, Rogers IS et al (2010) Incremental value of adenosine-induced stress myocardial perfusion imaging with dual-source CT at cardiac CT angiography. Radiology 254(2):410–419. doi: 10.1148/radiol.09091014 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Bamberg F, Klotz E, Flohr T, Becker A, Becker CR, Schmidt B et al (2010) Dynamic myocardial stress perfusion imaging using fast dual-source CT with alternating table positions: initial experience. Eur Radiol 20(5):1168–1173. doi: 10.1007/s00330-010-1715-9 CrossRefPubMedGoogle Scholar
  8. 8.
    Huber AM, Leber V, Gramer BM, Muenzel D, Leber A, Rieber J et al (2013) Myocardium: dynamic versus single-shot CT perfusion imaging. Radiology 269(2):378–386. doi: 10.1148/radiol.13121441 CrossRefPubMedGoogle Scholar
  9. 9.
    Dewey M, Zimmermann E, Deissenrieder F, Laule M, Dübel HP, Schlattmann P et al (2009) Noninvasive coronary angiography by 320-row computed tomography with lower radiation exposure and maintained diagnostic accuracy: comparison of results with cardiac catheterization in a head-to-head pilot investigation. Circulation 120(10):867–875. doi: 10.1161/CIRCULATIONAHA.109.859280 CrossRefPubMedGoogle Scholar
  10. 10.
    Kikuchi Y, Oyama-Manabe N, Naya M, Manabe O, Tomiyama Y, Sasaki T et al (2014) Quantification of myocardial blood flow using dynamic 320-row multi-detector CT as compared with 15O-H2O PET. Eur Radiol 24(7):1547–1556. doi: 10.1007/s00330-014-3164-3 CrossRefPubMedGoogle Scholar
  11. 11.
    Bamberg F, Becker A, Schwarz F, Marcus RP, Greif M, von Ziegler F et al (2011) Detection of hemodynamically significant coronary artery stenosis: incremental diagnostic value of dynamic CT-based myocardial perfusion imaging. Radiology 260(3):689–698. doi: 10.1148/radiol.11110638 CrossRefPubMedGoogle Scholar
  12. 12.
    Kim SM, Kim YN, Choe YH. (2013) Adenosine-stress dynamic myocardial perfusion imaging using 128-slice dual-source CT: optimization of the CT protocol to reduce the radiation dose. Int J Cardiovasc Imaging 29(4):875–884. doi: 10.1007/s10554-012-0138-x CrossRefPubMedGoogle Scholar
  13. 13.
    Kim SM, Cho YK, Choe YH (2014) Adenosine-stress dynamic myocardial perfusion imaging using 128-slice dual-source CT in patients with normal body mass indices: effect of tube voltage, tube current, and iodine concentration on image quality and radiation dose. Int J Cardiovasc Imaging 30(Suppl 2):95–103. doi: 10.1007/s10554-014-0524-7 CrossRefPubMedGoogle Scholar
  14. 14.
    Ziemer BP, Hubbard L, Lipinski J, Molloi S (2015) Dynamic CT perfusion measurement in a cardiac phantom. Int J Cardiovasc Imaging 31(7):1451–1459. doi: 10.1007/s10554-015-0700-4 CrossRefPubMedGoogle Scholar
  15. 15.
    Dewey M (2014) Cardiac CT, 2nd edn. Springer-Verlag, Berlin, XIII, 498 pGoogle Scholar
  16. 16.
    Stenzel F, Rief M, Zimmermann E, Greupner J, Richter F, Dewey M (2014) Contrast agent bolus tracking with a fixed threshold or a manual fast start for coronary CT angiography. Eur Radiol 24(6):1229–1238. doi: 10.1007/s00330-014-3148-3 CrossRefPubMedGoogle Scholar
  17. 17.
    Tomizawa N, Nojo T, Akahane M, Torigoe R, Kiryu S, Ohtomo K (2012) AdaptiveIterative dose reduction in coronary CT angiography using 320-row CT: assessment of radiation dose reduction and image quality. J Cardiovasc Comput Tomogr 6(5):318–324. doi: 10.1016/j.jcct.2012.02.009 CrossRefPubMedGoogle Scholar
  18. 18.
    Feger S, Rief M, Zimmermann E, Martus P, Schuijf JD, Blobel J et al (2015) The impact of different levels of adaptive iterative dose reduction 3D on image quality of 320-row coronary CT angiography: a clinical trial. PLoS One 10(5):e0125943. doi: 10.1371/journal.pone.0125943 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Kendziorra C, Meyer H, Dewey M (2014) Implementation of a phase detection algorithm for dynamic cardiac computed tomography analysis based on time dependent contrast agent distribution. PLoS One 9(12):e116103. doi: 10.1371/journal.pone.0116103 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Mehra VC, Valdiviezo C, Arbab-Zadeh A, Ko BS, Seneviratne SK, Cerci R et al (2011) A stepwise approach to the visual interpretation of CT-based myocardial perfusion. J Cardiovasc Comput Tomogr 5(6):357–369. doi: 10.1016/j.jcct.2011.10.010 CrossRefPubMedGoogle Scholar
  21. 21.
    Dewey M (ed) (2014) Cardiac CT, 2nd edn. Springer-Verlag, BerlinGoogle Scholar
  22. 22.
    Cerqueira MD, Weissman NJ, Dilsizian V, Jacobs AK, Kaul S, Laskey WK, et al. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. Circulation. 2002;105(4):539–542CrossRefPubMedGoogle Scholar
  23. 23.
    Rodriguez-Granillo GA, Rosales MA, Degrossi E, Rodriguez AE (2010) Signal density of left ventricular myocardial segments and impact of beam hardening artifact: implications for myocardial perfusion assessment by multidetector CT coronary angiography. Int J Cardiovasc Imaging 26(3):345–354. doi: 10.1007/s10554-009-9531-5 CrossRefPubMedGoogle Scholar
  24. 24.
    Cerqueira MD, Nguyen P, Staehr P, Underwood SR, Iskandrian AE (2008) Effects of age, gender, obesity, and diabetes on the efficacy and safety of the selective A2A agonist regadenoson versus adenosine in myocardial perfusion imaging integrated ADVANCE-MPI trial results. JACC 1(3):307–316. doi: 10.1016/j.jcmg.2008.02.003 PubMedGoogle Scholar
  25. 25.
    Feuchtner G, Goetti R, Plass A, Wieser M, Scheffel H, Wyss C et al (2011) Adenosine stress high-pitch 128-slice dual-source myocardial computed tomography perfusion for imaging of reversible myocardial ischemia: comparison with magnetic resonance imaging. Circ Cardiovasc Imaging 4(5):540–549. doi: 10.1161/CIRCIMAGING.110.961250 CrossRefPubMedGoogle Scholar
  26. 26.
    Gramer BM, Muenzel D, Leber V, von Thaden AK, Feussner H, Schneider A et al (2012) Impact of iterative reconstruction on CNR and SNR in dynamic myocardial perfusion imaging in an animal model. Eur Radiol 22(12):2654–2661. doi: 10.1007/s00330-012-2525-z CrossRefPubMedGoogle Scholar
  27. 27.
    Fujita M, Kitagawa K, Ito T, Shiraishi Y, Kurobe Y, Nagata M, et al. Dose reduction in dynamic CT stress myocardial perfusion imaging: comparison of 80-kV/370-mAs and 100-kV/300-mAs protocols. Eur Radiol. 2014;24(3):748–755. doi: 10.1007/s00330-013-3063-z CrossRefPubMedGoogle Scholar
  28. 28.
    Bischoff B, Bamberg F, Marcus R, Schwarz F, Becker HC, Becker A et al (2013) Optimal timing for first-pass stress CT myocardial perfusion imaging. Int J Cardiovasc Imaging. 29(2):435–442. doi: 10.1007/s10554-012-0080-y CrossRefPubMedGoogle Scholar
  29. 29.
    Blaimer M, Ponce IP, Breuer FA, Jakob PM, Griswold MA, Kellman P (2011) Temporal filtering effects in dynamic parallel MRI. Magn Reson Med 66(1):192–198. doi: 10.1002/mrm.CrossRefPubMedGoogle Scholar
  30. 30.
    Moore J DM, Wierzbicki M (2003) A high resolution dynamic heart model based on averaged MRI dataGoogle Scholar
  31. 31.
    Metz CT, Klein S, Schaap M, van Walsum T, Niessen WJ (2011) Nonrigid registration of dynamic medical imaging data using nD + t B-splines and a groupwise optimization approach. Medical image analysis 15(2):238–249. doi: 10.1016/ CrossRefPubMedGoogle Scholar
  32. 32.
    Smit EJ, Vonken EJ, van der Schaaf IC, Mendrik AM, Dankbaar JW, Horsch AD et al (2012) Timing-invariant reconstruction for deriving high-quality CT angiographic data from cerebral CT perfusion data. Radiology 263(1):216–225. doi: 10.1148/radiol.11111068 CrossRefPubMedGoogle Scholar
  33. 33.
    Mendrik AM, Vonken EJ, van Ginneken B, de Jong HW, Riordan A, van Seeters T et al (2011) TIPS bilateral noise reduction in 4D CT perfusion scans produces high-quality cerebral blood flow maps. Phys Med Biol 56(13):3857–3872. doi: 10.1088/0031-9155/56/13/008 CrossRefPubMedGoogle Scholar
  34. 34.
    Li Z, Yu L, Leng S, Williamson EE, Kotsenas AL, DeLone DR et al (2016) A robust noise reduction technique for time resolved CT. Med Phys 43(1):347. doi: 10.1118/1.4938576 CrossRefPubMedGoogle Scholar
  35. 35.
    NS K (1998) A system engineering approach to imaging. SPIE press, Bellingham, pp 541Google Scholar
  36. 36.
    Speidel MA, Bateman CL, Tao Y, Raval AN, Hacker TA, Reeder SB et al (2013) Reduction of image noise in low tube current dynamic CT myocardial perfusion imaging using HYPR processing: a time-attenuation curve analysis. Med Phys 40(1):011904. doi: 10.1118/1.4770283 CrossRefPubMedGoogle Scholar
  37. 37.
    Bhat S, Larina IV, Larin KV, Dickinson ME, Liebling M (2009) Multiple-cardiac-cycle noise reduction in dynamic optical coherence tomography of the embryonic heart and vasculature. Opt Lett 34(23):3704–3706CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    de Jong MC, Genders TS, van Geuns RJ, Moelker A, Hunink MG (2012) Diagnostic performance of stress myocardial perfusion imaging for coronary artery disease: a systematic review and meta-analysis. Eur Radiol 22(9):1881–1895. doi: 10.1007/s00330-012-2434-1 CrossRefPubMedPubMedCentralGoogle Scholar
  39. 38.
    Isola AA, Schmitt H, van Stevendaal U, Begemann PG, Coulon P, Boussel L et al (2011) Image registration and analysis for quantitative myocardial perfusion: application to dynamic circular cardiac CT. Phys Med Biol 56(18):5925–5947. doi: 10.1088/0031-9155/56/18/010 CrossRefPubMedGoogle Scholar
  40. 40.
    Ebersberger U, Marcus RP, Schoepf UJ, Lo GG, Wang Y, Blanke P et al (2014) Dynamic CT myocardial perfusion imaging: performance of 3D semi-automated evaluation software. Eur Radiol 24(1):191–199. doi: 10.1007/s00330-013-2997-5 CrossRefPubMedGoogle Scholar
  41. 41.
    Marwan M, Mettin C, Pflederer T, Seltmann M, Schuhback A, Muschiol G et al (2013) Very low-dose coronary artery calcium scanning with high-pitch spiral acquisition mode: comparison between 120-kV and 100-kV tube voltage protocols. J Cardiovasc Comput Tomogr 7(1):32–38. doi: 10.1016/j.jcct.2012.11.004 CrossRefPubMedGoogle Scholar
  42. 42.
    Deprez FC, Vlassenbroek A, Ghaye B, Raaijmakers R, Coche E (2013) Controversies about effects of low-kilovoltage MDCT acquisition on Agatston calcium scoring. J Cardiovasc Comput Tomogr 7(1):58–61. doi: 10.1016/j.jcct.2012.11.006 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • S. Feger
    • 1
  • A. Shaban
    • 1
  • S. Lukas
    • 1
  • C. Kendziorra
    • 2
  • M. Rief
    • 1
  • E. Zimmermann
    • 1
  • M. Dewey
    • 1
  1. 1.Department of RadiologyCharite Medical School BerlinBerlinGermany
  2. 2.BerlinGermany

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