The effect of iterative model reconstruction on coronary artery calcium quantification

  • Bálint Szilveszter
  • Hesham Elzomor
  • Mihály Károlyi
  • Márton Kolossváry
  • Rolf Raaijmakers
  • Kálmán Benke
  • Csilla Celeng
  • Andrea Bartykowszki
  • Zsolt Bagyura
  • Árpád Lux
  • Béla Merkely
  • Pál Maurovich-Horvat
Original Paper


Coronary artery calcium (CAC) scoring with computed tomography (CT) is an established tool for quantifying calcified atherosclerotic plaque burden. Despite the widespread use of novel image reconstruction techniques in CT, the effect of iterative model reconstruction on CAC score remains unclear. We sought to assess the impact of iterative model based reconstruction (IMR) on coronary artery calcium quantification as compared to the standard filtered back projection (FBP) algorithm and hybrid iterative reconstruction (HIR). In addition, we aimed to simulate the impact of iterative reconstruction techniques on calcium scoring based risk stratification of a larger asymptomatic population. We studied 63 individuals who underwent CAC scoring. Images were reconstructed with FBP, HIR and IMR and CAC scores were measured. We estimated the cardiovascular risk reclassification rate of IMR versus HIR and FBP in a larger asymptomatic population (n = 504). The median CAC scores were 147.7 (IQR 9.6–582.9), 107.0 (IQR 5.9–526.6) and 115.1 (IQR 9.3–508.3) for FBP, HIR and IMR, respectively. The HIR and IMR resulted in lower CAC scores as compared to FBP (both p < 0.001), however there was no difference between HIR and IMR (p = 0.855). The CAC score decreased by 7.2 % in HIR and 7.3 % in IMR as compared to FBP, resulting in a risk reclassification rate of 2.4 % for both HIR and IMR. The utilization of IMR for CAC scoring reduces the measured calcium quantity. However, the CAC score based risk stratification demonstrated modest reclassification in IMR and HIR versus FBP.


Coronary artery disease Computed tomography Iterative model reconstruction Coronary artery calcium scoring Risk stratification 


Compliance with ethical standards

The institutional ethics review board has approved our study. Participants provided written informed consent.

Conflict of interest

Rolf Raaijmakers is an employee of Philips HealthTech.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    Mensah GA, Moran AE, Roth GA, Narula J (2014) The global burden of cardiovascular diseases, 1990-2010. Global Heart 9(1):183–184. doi: 10.1016/j.gheart.2014.01.008 PubMedCrossRefGoogle Scholar
  2. 2.
    Fuster V (2014) Global burden of cardiovascular disease: time to implement feasible strategies and to monitor results. J Am Coll Cardiol 64(5):520–522. doi: 10.1016/j.jacc.2014.06.1151 PubMedCrossRefGoogle Scholar
  3. 3.
    Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, de Ferranti S, Despres JP, Fullerton HJ, Howard VJ, Huffman MD, Judd SE, Kissela BM, Lackland DT, Lichtman JH, Lisabeth LD, Liu S, Mackey RH, Matchar DB, McGuire DK, Mohler ER III, Moy CS, Muntner P, Mussolino ME, Nasir K, Neumar RW, Nichol G, Palaniappan L, Pandey DK, Reeves MJ, Rodriguez CJ, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Willey JZ, Woo D, Yeh RW, Turner MB, American Heart Association Statistics C, Stroke Statistics S (2015) Heart disease and stroke statistics–2015 update: a report from the American Heart Association. Circulation 131(4):e29–e322. doi: 10.1161/CIR.0000000000000152 PubMedCrossRefGoogle Scholar
  4. 4.
    Nichols M, Townsend N, Scarborough P, Rayner M (2014) Cardiovascular disease in Europe 2014: epidemiological update. Eur Heart J 35(42):2950–2959. doi: 10.1093/eurheartj/ehu299 PubMedCrossRefGoogle Scholar
  5. 5.
    Petretta M, Daniele S, Acampa W, Imbriaco M, Pellegrino T, Messalli G, Xhoxhi E, Del Prete G, Nappi C, Accardo D, Angeloni F, Bonaduce D, Cuocolo A (2012) Prognostic value of coronary artery calcium score and coronary CT angiography in patients with intermediate risk of coronary artery disease. Int J Cardiovasc Imaging 28(6):1547–1556. doi: 10.1007/s10554-011-9948-5 PubMedCrossRefGoogle Scholar
  6. 6.
    Hou ZH, Lu B, Gao Y, Jiang SL, Wang Y, Li W, Budoff MJ (2012) Prognostic value of coronary CT angiography and calcium score for major adverse cardiac events in outpatients. JACC Cardiovasc Imaging 5(10):990–999. doi: 10.1016/j.jcmg.2012.06.006 PubMedCrossRefGoogle Scholar
  7. 7.
    Budoff MJ, Shaw LJ, Liu ST, Weinstein SR, Mosler TP, Tseng PH, Flores FR, Callister TQ, Raggi P, Berman DS (2007) Long-term prognosis associated with coronary calcification: observations from a registry of 25,253 patients. J Am Coll Cardiol 49(18):1860–1870. doi: 10.1016/j.jacc.2006.10.079 PubMedCrossRefGoogle Scholar
  8. 8.
    Puchner SB, Ferencik M, Karolyi M, Do S, Maurovich-Horvat P, Kauczor HU, Hoffmann U, Schlett CL (2013) The effect of iterative image reconstruction algorithms on the feasibility of automated plaque assessment in coronary CT angiography. Int J Cardiovasc Imaging 29(8):1879–1888. doi: 10.1007/s10554-013-0281-z PubMedCrossRefGoogle Scholar
  9. 9.
    Halpern EJ, Gingold EL, White H, Read K (2014) Evaluation of coronary artery image quality with knowledge-based iterative model reconstruction. Acad Radiol 21(6):805–811. doi: 10.1016/j.acra.2014.02.017 PubMedCrossRefGoogle Scholar
  10. 10.
    Yuki H, Utsunomiya D, Funama Y, Tokuyasu S, Namimoto T, Hirai T, Itatani R, Katahira K, Oshima S, Yamashita Y (2014) Value of knowledge-based iterative model reconstruction in low-kV 256-slice coronary CT angiography. J Cardiovasc Comput Tomogr 8(2):115–123. doi: 10.1016/j.jcct.2013.12.010 PubMedCrossRefGoogle Scholar
  11. 11.
    Oda S, Utsunomiya D, Funama Y, Katahira K, Honda K, Tokuyasu S, Vembar M, Yuki H, Noda K, Oshima S, Yamashita Y (2014) A knowledge-based iterative model reconstruction algorithm: can super-low-dose cardiac CT be applicable in clinical settings? Acad Radiol 21(1):104–110. doi: 10.1016/j.acra.2013.10.002 PubMedCrossRefGoogle Scholar
  12. 12.
    Willemink MJ, Takx RA, de Jong PA, Budde RP, Bleys RL, Das M, Wildberger JE, Prokop M, Buls N, de Mey J, Schilham AM, Leiner T (2014) The impact of CT radiation dose reduction and iterative reconstruction algorithms from four different vendors on coronary calcium scoring. Eur Radiol 24(9):2201–2212. doi: 10.1007/s00330-014-3217-7 PubMedCrossRefGoogle Scholar
  13. 13.
    Funabashi N, Irie R, Aiba M, Morimoto R, Kabashima T, Fujii S, Uehara M, Ozawa K, Takaoka H, Kobayashi Y (2013) Adaptive-Iterative-Dose-Reduction 3D with multisector-reconstruction method in 320-slice CT may maintain accurate-measurement of the Agatston-calcium-score of severe-calcification even at higher pulsating-beats and low tube-current in vitro. Int J Cardiol 168(1):601–603. doi: 10.1016/j.ijcard.2013.01.230 PubMedCrossRefGoogle Scholar
  14. 14.
    Willemink MJ, Takx RA, de Jong PA, Budde RP, Bleys RL, Das M, Wildberger JE, Prokop M, Buls N, de Mey J, Leiner T, Schilham AM (2014) Computed tomography radiation dose reduction: effect of different iterative reconstruction algorithms on image quality. J Comput Assist Tomogr 38(6):815–823. doi: 10.1097/RCT.0000000000000128 PubMedCrossRefGoogle Scholar
  15. 15.
    Bagyura Z, Kiss L, Edes E, Lux A, Polgar L, Soos P, Szenczi O, Szelid Z, Vadas R, Jozan P, Bagdy G, Merkely B (2014) Cardiovascular screening programme in the Central Hungarian region. The Budakalasz study. Orv Hetil 155(34):1344–1352. doi: 10.1556/OH.2014.29969 PubMedCrossRefGoogle Scholar
  16. 16.
    Christner JA, Kofler JM, McCollough CH (2010) Estimating effective dose for CT using dose-length product compared with using organ doses: consequences of adopting International Commission on Radiological Protection publication 103 or dual-energy scanning. AJR Am J Roentgenol 194(4):881–889. doi: 10.2214/AJR.09.3462 PubMedCrossRefGoogle Scholar
  17. 17.
    Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M Jr, Detrano R (1990) Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol 15(4):827–832PubMedCrossRefGoogle Scholar
  18. 18.
    Raggi P, Shaw LJ, Berman DS, Callister TQ (2004) Prognostic value of coronary artery calcium screening in subjects with and without diabetes. J Am Coll Cardiol 43(9):1663–1669. doi: 10.1016/j.jacc.2003.09.068 PubMedCrossRefGoogle Scholar
  19. 19.
    Rasmussen T, Zacho M, Kourmaeva D, Kober L, Kofoed KF (2011) Coronary artery calcium score in cardiac CT increases the prognostic information of selected patients. Ugeskr Laeger 173(36):2190–2195PubMedGoogle Scholar
  20. 20.
    Folsom AR, Kronmal RA, Detrano RC, O’Leary DH, Bild DE, Bluemke DA, Budoff MJ, Liu K, Shea S, Szklo M, Tracy RP, Watson KE, Burke GL (2008) Coronary artery calcification compared with carotid intima-media thickness in the prediction of cardiovascular disease incidence: the Multi-Ethnic Study of Atherosclerosis (MESA). Arch Intern Med 168(12):1333–1339. doi: 10.1001/archinte.168.12.1333 PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    Shaw LJ, Raggi P, Schisterman E, Berman DS, Callister TQ (2003) Prognostic value of cardiac risk factors and coronary artery calcium screening for all-cause mortality. Radiology 228(3):826–833. doi: 10.1148/radiol.2283021006 PubMedCrossRefGoogle Scholar
  22. 22.
    Vliegenthart R, Oudkerk M, Hofman A, Oei HH, van Dijck W, van Rooij FJ, Witteman JC (2005) Coronary calcification improves cardiovascular risk prediction in the elderly. Circulation 112(4):572–577. doi: 10.1161/CIRCULATIONAHA.104.488916 PubMedCrossRefGoogle Scholar
  23. 23.
    Kondos GT, Hoff JA, Sevrukov A, Daviglus ML, Garside DB, Devries SS, Chomka EV, Liu K (2003) Electron-beam tomography coronary artery calcium and cardiac events: a 37-month follow-up of 5635 initially asymptomatic low- to intermediate-risk adults. Circulation 107(20):2571–2576. doi: 10.1161/01.CIR.0000068341.61180.55 PubMedCrossRefGoogle Scholar
  24. 24.
    Greenland P, LaBree L, Azen SP, Doherty TM, Detrano RC (2004) Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals. JAMA 291(2):210–215. doi: 10.1001/jama.291.2.210 PubMedCrossRefGoogle Scholar
  25. 25.
    Criqui MH, Denenberg JO, Ix JH, McClelland RL, Wassel CL, Rifkin DE, Carr JJ, Budoff MJ, Allison MA (2014) Calcium density of coronary artery plaque and risk of incident cardiovascular events. JAMA 311(3):271–278. doi: 10.1001/jama.2013.282535 PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Kurata A, Dharampal A, Dedic A, de Feyter PJ, Krestin GP, Dijkshoorn ML, Nieman K (2013) Impact of iterative reconstruction on CT coronary calcium quantification. Eur Radiol 23(12):3246–3252. doi: 10.1007/s00330-013-3022-8 PubMedCrossRefGoogle Scholar
  27. 27.
    van Osch JA, Mouden M, van Dalen JA, Timmer JR, Reiffers S, Knollema S, Greuter MJ, Ottervanger JP, Jager PL (2014) Influence of iterative image reconstruction on CT-based calcium score measurements. Int J Cardiovasc Imaging 30(5):961–967. doi: 10.1007/s10554-014-0409-9 PubMedGoogle Scholar
  28. 28.
    Gebhard C, Fiechter M, Fuchs TA, Ghadri JR, Herzog BA, Kuhn F, Stehli J, Muller E, Kazakauskaite E, Gaemperli O, Kaufmann PA (2013) Coronary artery calcium scoring: influence of adaptive statistical iterative reconstruction using 64-MDCT. Int J Cardiol 167(6):2932–2937. doi: 10.1016/j.ijcard.2012.08.003 PubMedCrossRefGoogle Scholar
  29. 29.
    Takx RA, Willemink MJ, Nathoe HM, Schilham AM, Budde RP, de Jong PA, Leiner T (2014) The effect of iterative reconstruction on quantitative computed tomography assessment of coronary plaque composition. Int J Cardiovasc Imaging 30(1):155–163. doi: 10.1007/s10554-013-0293-8 PubMedCrossRefGoogle Scholar
  30. 30.
    Willemink MJ, Vliegenthart R, Takx RA, Leiner T, Budde RP, Bleys RL, Das M, Wildberger JE, Prokop M, Buls N, de Mey J, Schilham AM, de Jong PA (2014) Coronary artery calcification scoring with state-of-the-art CT scanners from different vendors has substantial effect on risk classification. Radiology 273(3):695–702. doi: 10.1148/radiol.14140066 PubMedCrossRefGoogle Scholar
  31. 31.
    Tatsugami F, Higaki T, Fukumoto W, Kaichi Y, Fujioka C, Kiguchi M, Yamamoto H, Kihara Y, Awai K (2015) Radiation dose reduction for coronary artery calcium scoring at 320-detector CT with adaptive iterative dose reduction 3D. Int J Cardiovasc Imaging 31(5):1045–1052. doi: 10.1007/s10554-015-0637-7 PubMedCrossRefGoogle Scholar
  32. 32.
    Yoo SM, Lee HY, White CS (2014) Screening coronary CT angiography: possibilities and pitfalls. Int J Cardiovasc Imaging 30(8):1599–1601. doi: 10.1007/s10554-014-0495-8 PubMedCrossRefGoogle Scholar
  33. 33.
    Tomizawa N, Nojo T, Inoh S, Nakamura S (2015) Difference of coronary artery disease severity, extent and plaque characteristics between patients with hypertension, diabetes mellitus or dyslipidemia. Int J Cardiovasc Imaging 31(1):205–212. doi: 10.1007/s10554-014-0542-5 PubMedCrossRefGoogle Scholar
  34. 34.
    Tullos BW, Sung JH, Lee JE, Criqui MH, Mitchell ME, Taylor HA (2013) Ankle-brachial index (ABI), abdominal aortic calcification (AAC), and coronary artery calcification (CAC): the Jackson heart study. Int J Cardiovasc Imaging 29(4):891–897. doi: 10.1007/s10554-012-0145-y PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Bálint Szilveszter
    • 1
  • Hesham Elzomor
    • 1
  • Mihály Károlyi
    • 1
  • Márton Kolossváry
    • 1
  • Rolf Raaijmakers
    • 2
  • Kálmán Benke
    • 1
  • Csilla Celeng
    • 1
  • Andrea Bartykowszki
    • 1
  • Zsolt Bagyura
    • 1
  • Árpád Lux
    • 1
  • Béla Merkely
    • 1
  • Pál Maurovich-Horvat
    • 1
  1. 1.MTA-SE Lendület Cardiovascular Imaging Research Group, Heart and Vascular CenterSemmelweis UniversityBudapestHungary
  2. 2.Philips HealthTechBestThe Netherlands

Personalised recommendations