Skip to main content
SpringerLink
Log in

Computer-aided simple triage (CAST) for coronary CT angiography (CCTA)

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

Abstract

Purpose

Following a recent introduction of computer-aided simple triage (CAST) as a new subclass of computer-aided detection/diagnosis (CAD), we present a CAST software system for a fully automatic initial interpretation of coronary CT angiography (CCTA). We show how the system design and diagnostic performance make it CAST-compliant and suitable for chest pain patient triage in emergency room (ER).

Methods

The processing performed by the system consists of three major steps: segmentation of coronary artery tree, labeling of major coronary arteries, and detection of significant stenotic lesions (causing > 50% stenosis). In addition, the system performs an automatic image quality assessment to discards low-quality studies. For multiphase studies, the system automatically chooses the best phase for each coronary artery. Clinical evaluation results were collected in 14 independent trials that included more than 2000 CCTA studies. Automatic diagnosis results were compared with human interpretation of the CCTA and to cath lab results.

Results

The presented system performs a fully automatic initial interpretation of CCTA without any human interaction and detects studies with significant coronary artery disease. The system demonstrated higher than 90% per patient sensitivity and 40–70% per patient specificity. For the chest pain, ER population, the specificity was 60–70%, yielding higher than 98% NPV.

Conclusions

The diagnostic performance of the presented CCTA CAD system meets the CAST requirements, thus enabling efficient, 24/7 utilization of CCTA for chest pain patient triage in ER. This is the first fully operational, clinically validated, CAST-compliant CAD system for a fully automatic analysis of CCTA and detection of significant stenosis.

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

Similar content being viewed by others

References

  1. Goldenberg R, Peled N (2011) Computer aided simple triage. Int J Comput Assist Radiol Surg 6(5): 705–711

    Article  PubMed  Google Scholar 

  2. Gallagher M et al (2007) The diagnostic accuracy of 64-Slice computed tomography coronary angiography compared with stress nuclear imaging in emergency department low-risk chest pain patients. Ann Emerg Med 49(2): 125–136

    Article  PubMed  Google Scholar 

  3. Hulten E, Carbonaro S, Petrillo S, Mitchell J, Villines T (2011) Prognostic value of cardiac computed tomography angiography: a systematic review and meta-analysis. J Am Coll Cardiol 57: 1237–1247

    Article  PubMed  Google Scholar 

  4. Goldstein J, Gallagher M, O’Neill W, Ross M, O’Neil B, Raff G (2007) A randomized controlled trial of multi-slice coronary computed tomography for evaluation of acute chest pain. J Am Coll Cardiol 49: 863–871

    Article  PubMed  Google Scholar 

  5. Anderson J, Adams C, Antman E et al (2007) ACC/AHA 2007 guidelines for the management of patients with unstable angina/non-ST-Elevation myocardial infarction: a report of the American college of cardiology. J Am Coll Cardiol 50(7): e1–e157

    Article  PubMed  Google Scholar 

  6. Hendel R, Patel M, Kramer C et al (2006) ACCF/ACR/SCCT/ SCMR/ASNC/NASCI/SCAI/SIR 2006 appropriateness criteria for cardiac computed tomography and cardiac magnetic resonance imaging: a report of the American college of cardiology foundation quality strategic directions committee. J Am Coll Cardiol 48(7): 1475–1497

    Article  PubMed  Google Scholar 

  7. Duda RO, Hart PE (1972) Use of the Hough transform to detect lines and curves in pictures. Commun ACM 15(1): 11–15

    Article  Google Scholar 

  8. Hough V, Paul C (1962) Methods and means for recognizing complex patterns. U.S. Patent 3069654

  9. Goldenberg R, Kimmel R, Rivlin E, Rudzsky M (2001) Fast geodesic active contours. IEEE Trans Image Proces 10(10): 1467–1475

    Article  CAS  Google Scholar 

  10. Laguitton S, Grady L, Lesage D, Funka-lea G (2008) Automatic coronary tree modeling. Blood Vessel Midas J. http://hdl.handle.net/10380/1426

  11. Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: MICCAI’98, Cambridge, MA

  12. Lucas E (1882) Récréations mathématiques, vol I. Gauthier-Villars et fils, Imprimeurs-Libraires, Paris

    Google Scholar 

  13. Begelman G, Goldenberg R, Levanon S, Ohayon S, Walach E (2011) Creating a blood vessel tree from imaging data. United States Patent 7,983,459

  14. Kirbas C, Quek FKH (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. Zhang X, Collins S, Sonka M (1995) Tree pruning strategy in automated detection of coronary trees in cineangiograms. In: International conference on image processing, Washington

  17. Kelm B, Mittal S, Zheng Y, Tsymbal A, Bernhardt D, Vega-Higuera F, Zhou S, Meer P, Comaniciu D (2011) Detection, grading and classification of coronary stenoses in computed tomography angiography. In: MICCAI’11, Toronto

  18. Yang G, et al (2011) Automatic labeling of coronary artery tree in CCTA. In: Computing in cardiology, Hangzhou

  19. Kanitsar A, Fleischmann D, Wegenkittl R, Gröller ME (2005) Diagnostic relevant visualization of vascular structures. In: Scientific visualization: the visual extraction of knowledge from data. Springer, pp 207–228

  20. Xu C, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Proces 7(3): 359–369

    Article  CAS  Google Scholar 

  21. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Proces 10(2): 266–277

    Article  CAS  Google Scholar 

  22. 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. United States Patent 7,940,977

  23. Zadeh L (1965) Fuzzy sets. Inf Control 8(3): 338–353

    Article  Google Scholar 

  24. Dey D, Schepis T, Marwan M, Slomka P, Berman D, Achenbach S (2010) Automated three-dimensional quantification of noncalcified coronary plaque from coronary CT angiography: comparison with intravascular US. Radiology 257(2): 516–522

    Article  PubMed  Google Scholar 

  25. Wang Y, Liatsis P (2009) A fully automated framework for segmentation and stenosis quantification of coronary arteries in 3D CTA imaging. In: Second International Conference on Developments in eSystems Engineering Abu Dhabi

  26. Cline HE, Krishnan K, Napel S, Rubin GD, Turner WD, Avila RS (2009) Automated coronary CT angiography plaque-lumen segmentation. Proc SPIE Med Imaging 7260: 726003–72600310

    Google Scholar 

  27. Boogers MJ et al (2010) Automated quantification of stenosis severity on 64-slice CT: a comparison with quantitative coronary angiography. JACC Cardiovasc imaging 3(7): 699–709

    Article  PubMed  Google Scholar 

  28. Rinck D, Kruger S, Reimann A, Scheuering M (2006) Shape-based segmentation and visualization techniques for evaluation of atherosclerotic plaques in coronary artery disease. Proc SPIE Int Soc Opt Eng 6141: 61410G–61410G-9

    Google Scholar 

  29. Levanon S, Goldenberg R, Levy M (2011) Method and system for automatic quality control used in computerized analysis of CT angiography. United States Patent 7,940,970

  30. Weisman J, Yuz M (2008) Fully automated coronary CTA analysis. In: SCCT’08, Orlando

  31. Lopez C, Weissman G, Joshi S, Weigold W (2008) Automatic computerized evaluation of 64 multislice coronary CTA. A comparison between COR Analyzer II software and visual evaluation. In: SCCT’08, Orlando

  32. Poon M (2009) The role of computed aided diagnosis in the management of acute chest pain. In: SCCT’09, Orlando

  33. Malhotra V (2009) COR analyzer diagnostic performance analysis. In: SCCT’09, Orlando

  34. Tyagi G, et al (2009) Segmental accuracy of an automated analyzer of coronary CT angiography (CCTA) in ED patients: (initial experience). In: RSNA’09, Chicago

  35. Arnoldi E, Gebregziabher M, Schoepf U, Goldenberg R, Ramos-Duran L, Zwerner P, Nikolaou K, Reiser M, Costello P, Thilo C (2010) Automated computer-aided stenosis detection at coronary CT angiography: initial experience. Eur Radiol 20(5): 1160–1167

    Article  PubMed  Google Scholar 

  36. Daubert M, Malhotra V, Ferraro S, Goldenberg R, Kam M, Wu H, Kam D, Minton A, Poon M (2010) Computer-aided analysis of 64-slice coronary computed tomography angiography: a comparison with manual interpretation. In: SCCT’10, Las Vegas

  37. Malhotra V, Poon M, Krishnan U, Mcnerthney M, Goldenberg R, WN (2010) Computer aided detection for coronary CT angiography in low to intermediate risk population. In: SCCT’10, Las Vegas

  38. Mehta C, et al (2010) Validation of an automated cardiac CT angiography analysis system: initial experience at an academic center. In: SCCT’10, Las Vegas

  39. Anders K, Petit I, Achenbach S, Pflederer T (2010) Diagnostic utility of automated stenosis detection in dual source CT coronary angiography as a stand alone or add-on tool. In: SCCT’10, Las Vegas

  40. Halpern E, Halpern D (2010) Diagnosis of coronary stenosis with CT angiography: comparison of automated computer diagnosis with expert readings. Acad Radiol 18(3): 324–333

    Article  Google Scholar 

  41. Henzler T, Meyer M, Apfaltrer P, Schönberg S, Fink C (2012) Computed-aided stenosis detection on coronary CT angiography in chest pain patients with an intermediate pre-test likelihood for acute coronary syndrome. In: ECR’12, Vienna

  42. Kang K, Chang H, Shim H, Kim Y, Choi B, Yang W, Shim J, Ha J, Chung N (2012) Feasibility of an automatic computer-assisted algorithm for the detection of significant coronary artery disease in patients presenting with acute chest pain. Eur J Radiol 81(4): e640–e646

    Article  PubMed  Google Scholar 

  43. Schaap M, Metz C et al (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 

  44. Rotterdam Coronary Artery Algorithm Evaluation Framework, Biomedical Imaging Group Rotterdam, [Online]. Available: http://coronary.bigr.nl/centerlines/results/results.php

  45. Sansoni E, Schoepf U, Nance J, Barraza J, Henzler T, Deml K (2010) Computer aided detection of coronary artery disease in the CT-based triage of acute chest pain patients—validation by patient outcome. In: RSNA’10, Chicago

Download references

Author information

Authors and Affiliations

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

    Roman Goldenberg, Dov Eilot, Grigory Begelman, Eugene Walach & Eyal Ben-Ishai

  2. IBM Research Haifa Lab, Haifa, Israel

    Eugene Walach

  3. Department of Radiology, Lady Davis Carmel Medical Center, Haifa, Israel

    Nathan Peled

Authors
  1. Roman Goldenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dov Eilot
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Grigory Begelman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eugene Walach
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eyal Ben-Ishai
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nathan Peled
    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

Goldenberg, R., Eilot, D., Begelman, G. et al. Computer-aided simple triage (CAST) for coronary CT angiography (CCTA). Int J CARS 7, 819–827 (2012). https://doi.org/10.1007/s11548-012-0684-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-012-0684-7

Keywords

Search

Navigation