Skip to main content

Advertisement

SpringerLink
Log in

Fully automatic model-based calcium segmentation and scoring in coronary CT angiography

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The paper presents new methods for automatic coronary calcium detection, segmentation and scoring in coronary CT angiography (cCTA) studies.

Methods

Calcium detection and segmentation are performed by modeling image intensity profiles of coronary arteries. The scoring algorithm is based on a simulated unenhanced calcium score (CS) CT image, constructed by virtually removing the contrast media from cCTA. The methods are implemented as part of a fully automatic system for CS assessment from cCTA.

Results

The system was tested in two independent clinical trials on 263 studies and demonstrated 0.95/0.91 correlation between the CS computed from cCTA and the standard Agatston score derived from unenhanced CS CT. The mean absolute percent difference (MAPD) of 36/39 % between the two scores lies within the error range of the standard CS CT (15–65 %).

Conclusions

High diagnostic performance, combined with the benefits of the fully automatic solution, suggests that the proposed technique can be used to eliminate the need in a separate CS CT scan as part of the cCTA examination, thus reducing the radiation exposure and simplifying the procedure.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Notes

  1. The described method for calcium segmentation is patent pending.

  2. The described method for calcium scoring is patent pending.

References

  1. Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M, Detrano R (1990) Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol 15(4):827–832

    Google Scholar 

  2. Otton JM, Lønborg JT, Boshell D, Feneley M, Hayen A, Sammel N, Sesel K, Bester L, McCrohon J (2012) A method for coronary artery calcium scoring using contrast-enhanced computed tomography. J Cardiovasc Comput Tomogr 6(1):37–44

    Article  PubMed  Google Scholar 

  3. van der Bijl N, Joemai RM, Geleijns J, Bax JJ, Schuijf JD, de Roos A, Kroft LJ (2010) Assessment of Agatston coronary artery calcium score using contrast-enhanced CT coronary angiography. AJR Am J Roentgenol 195(6):1299–1305

    Article  PubMed  Google Scholar 

  4. Glodny B, Helmel B, Trieb T, Schenk C, Taferner B, Unterholzner V, Strasak A, Petersen J (2009) A method for calcium quantification by means of CT coronary angiography using 64-multidetector CT: very high correlation with Agatston and volume scores. Eur Radiol 19(7):1661–1668

    Article  PubMed  Google Scholar 

  5. Mühlenbruch G, Wildberger JE, Koos R, Das M, Flohr TG, Niethammer M, Weiss C, Gunther RW, Mahnken AH (2005) Coronary calcium scoring using 16-row multislice computed tomography: nonenhanced versus contrast-enhanced studies in vitro and in vivo. Invest Radiol 40(3):148–154

    Article  PubMed  Google Scholar 

  6. Hong C, Becker CR, Schoepf UJ, Ohnesorge B, Bruening R, Reiser MF (2002) Coronary artery calcium: absolute quantification in nonenhanced and contrast-enhanced multi-detector row CT studies. Radiology 223(2):474–480

    Article  PubMed  Google Scholar 

  7. Bischoff B, Kantert C, Meyer T, Hadamitzky M, Martinoff S, Schomig A, Hausleiter J (2012) Cardiovascular risk assessment based on the quantification of coronary calcium in contrast-enhanced coronary computed tomography angiography. Eur Heart J Cardiovasc Imaging 13(6):468–475

    Article  PubMed  Google Scholar 

  8. Tessmann M, Higuera FV, Fritz D, Scheuering M, Greiner G (2008) Learning-based detection of stenotic lesions in coronary CT data. In: Proceedings of the vision, modeling, and visualization conference. VMV’08, pp 189–198

  9. Mittal S, Zheng Y, Georgescu B, Vega-Higuera F, Zhou SK, Meer P, Comaniciu D (2010) Fast automatic detection of calcified coronary lesions in 3d cardiac CT images. In: Proceedings of the first international conference on Machine learning in medical imaging. MLMI’10. Springer, Berlin, pp 1–9

  10. Moselewski F, Ferencik M, Achenbach S, Abbara S, Cury RC, Booth SL, Jang IK, Brady TJ, Hoffmann U (2006) Threshold-dependent variability of coronary artery calcification measurements—implications for contrast-enhanced multi-detector row-computed tomography. Eur J Radiol 57(3):390–395

    Article  PubMed  Google Scholar 

  11. Goldenberg R, Eilot D, Begelman G, Walach E, Ben-Ishai E, Peled N (2012) Computer-aided simple triage (CAST) for coronary CT angiography (CCTA). Int J Comput Assist Radiol Surg 7(6):819–827

    Article  PubMed  Google Scholar 

  12. Begelman G, Goldenberg R, Levanon S, Ohayon S, Walach E (2011) Creating a blood vessel tree from imaging data, US Patent 7983459

  13. Begelman G, Goldenberg R, Levanon S, Ohayon S, Walach E (2011) Method and system for automatic analysis of blood vessel structures to identify calcium or soft plaque pathologies. US Patent 7940977

  14. Kirbas C, Quek F (2004) A review of vessel extraction techniques and algorithms. ACM Comput Surv 36(2):81–121

    Article  Google Scholar 

  15. Lesage D, Angelini ED, Bloch I, Funka-Lea G (2009) A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med Image Anal 13(6):819–845

    Article  PubMed  Google Scholar 

  16. Kanitsar A, Fleischmann D, Wegenkittl R, Felkel P, Gröller ME (2002) CPR–curved planar reformation. In: IEEE visualization, pp 37–44

  17. Krissian K, Malandain G, Ayache N, Vaillant R, Trousset Y (2000) Model-based detection of tubular structures in 3-D images. Comput Vis Image Underst 80(2):130–171

    Article  Google Scholar 

  18. Worz S, Rohr K (2007) Segmentation and quantification of human vessels using a 3-D cylindrical intensity model. IEEE Trans Image Process 16(8):1994–2004

    Article  PubMed  Google Scholar 

  19. Coleman TF, Li Y (1996) A reflective Newton method for minimizing a quadratic function subject to bounds on some of the variables. SIAM J Optim 6(4):1040–1058

    Article  Google Scholar 

  20. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Proceedings of the sixth international conference on computer vision. ICCV’98. IEEE Computer Society, Washington, DC, pp 839–846

  21. Rutten A, Išgum I, Prokop M (2008) Coronary calcification: effect of small variation of scan starting position on Agatston, volume, and mass scores. Radiology 246(1):90–98

    Article  PubMed  Google Scholar 

  22. Schaap M, Metz C, van Walsum T, van der Giessen A, Weustink A, Mollet N, Bauer C, Bogunović H, Castro C, Deng X, Dikici E, O’Donnell T, Frenay M, Friman O, Hernández Hoyos M, Kitslaar P, Krissian K, Kühnel C, Luengo-Oroz M, Orkisz M, Smedby Ö, Styner M, Szymczak A, Tek H, Wang C, Warfield S, Zhang Y, Zambal S, Krestin G, Niessen W (2009) Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Med Image Anal 13(5):701–714

    Article  PubMed  Google Scholar 

  23. BIGR (2011) Rotterdam Coronary Artery Algorithm Evaluation Framework. http://coronary.bigr.nl/centerlines/results/results.php Online, Biomedical Imaging Group Rotterdam, Erasmus MC

  24. Rubinshtein R, Eilot D, Halon D, Goldenberg R, Gaspar T, Lewis B, Peled N (2012) Automatic assessment of calcium score from contrast-enhanced 256-row coronary CT angiography. J Am Coll Cardiol 59(13s1):E1185

    Google Scholar 

  25. Ebersberger U, Eilot D, Goldenberg R, Lev A, Spears JR, Rowe GW, Gallagher NY, Halligan WT, Blanke P, Makowski MR, Krazinski AW, Silverman JR, Bamberg F, Leber AW, Hoffmann E, Schoepf UJ (2013) Fully automated derivation of coronary artery calcium scores and cardiovascular risk assessment from contrast medium-enhanced coronary CT angiography studies. Eur Radiol 23(3):650–657

    Article  PubMed  Google Scholar 

  26. Rumberger JA, Brundage BH, Rader DJ, Kondos G (1999) Electron beam computed tomographic coronary calcium scanning: a review and guidelines for use in asymptomatic persons. Mayo Clin Proc 74(3):243–252

    Article  CAS  PubMed  Google Scholar 

  27. Takahashi N, Bae KT (2003) Quantification of coronary artery calcium with multi-detector row CT: assessing interscan variability with different tube currents pilot study. Radiology 228(1):101–106

    Article  PubMed  Google Scholar 

  28. Mao S, Bakhsheshi H, Lu B, Liu SC, Oudiz RJ, Budoff MJ (2001) Effect of electrocardiogram triggering on reproducibility of coronary artery calcium scoring. Radiology 220(3):707–711

    Article  CAS  PubMed  Google Scholar 

  29. Stanford W, Thompson BH, Burns TL, Heery SD, Burr MC (2004) Coronary artery calcium quantification at multi-detector row helical CT versus electron-beam CT. Radiology 230(2):397–402

    Article  PubMed  Google Scholar 

  30. Reinsch N, Mahabadi A, Lehmann N, Möhlenkamp S, Hoefs C, Sievers B, Budde T, Seibel R, Jckel K, Erbel R (2012) Comparison of dual-source and electron-beam CT for the assessment of coronary artery calcium scoring. Br J Radiol 85(1015):e300–e306

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Yoon HC, Goldin JG, Greaser LE, Sayre J, Fonarow GC (2000) Interscan variation in coronary artery calcium quantification in a large asymptomatic patient population. AJR Am J Roentgenol 174(3):803–809

    Article  CAS  PubMed  Google Scholar 

Download references

Conflict of interest

The authors declare that they have no conflict of interest

Author information

Authors and Affiliations

  1. Rcadia Medical Imaging, 157 Yafo Str., 35251 , Haifa, Israel

    Dov Eilot & Roman Goldenberg

Authors
  1. Dov Eilot
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roman Goldenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roman Goldenberg.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Eilot, D., Goldenberg, R. Fully automatic model-based calcium segmentation and scoring in coronary CT angiography. Int J CARS 9, 595–608 (2014). https://doi.org/10.1007/s11548-013-0955-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-013-0955-y

Keywords

Search

Navigation