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1. S. Yusuf, S. Reddy, S. Ounpuu, and S. Anand, “Global burden of cardiovascular diseases: Part I: General considerations, the epidemiologic transition, risk factors, and impact of urbanization,” Circulation 104, 27462753 (2001).
2. S. Achenbach, U. Ropers, A. Kuettner, K. Anders, T. Pflederer, S. Komatsu, W. Bautz, W. G. Daniel, and D. Ropers, “Randomized comparison of 64-slice single- and dual-source computed tomography coronary angiography for the detection of coronary artery disease,” JACC Cardiovasc. Imaging 1, 177186 (2008).
3. M. J. Budoff, D. Dowe, J. G. Jollis, M. Gitter, J. Sutherland, E. Halamert, M. Scherer, R. Bellinger, A. Martin, R. Benton, A. Delago, and J. K. Min, “Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: Results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial,” J. Am. Coll. Cardiol. 52, 17241732 (2008).
4. J. Hausleiter, T. Meyer, M. Hadamitzky, M. Zankl, P. Gerein, K. Dorrler, A. Kastrati, S. Martinoff, and A. Schomig, “Non-invasive coronary computed tomographic angiography for patients with suspected coronary artery disease: The Coronary Angiography by Computed Tomography with the Use of a Submillimeter resolution (CACTUS) trial,” Eur. Heart J. 28, 30343041 (2007).
5. W. B. Meijboom, C. A. van Mieghem, N. R. Mollet, F. Pugliese, A. C. Weustink, N. van Pelt, F. Cademartiri, K. Nieman, E. Boersma, P. de Jaegere, G. P. Krestin, and P. J. de Feyter, “64-slice computed tomography coronary angiography in patients with high, intermediate, or low pretest probability of significant coronary artery disease,” J. Am. Coll. Cardiol. 50, 14691475 (2007).
6. J. M. Miller, C. E. Rochitte, M. Dewey, A. Arbab-Zadeh, H. Niinuma, I. Gottlieb, N. Paul, M. E. Clouse, E. P. Shapiro, J. Hoe, A. C. Lardo, D. E. Bush, A. de Roos, C. Cox, J. Brinker, and J. A. Lima, “Diagnostic performance of coronary angiography by 64-row CT,” N. Engl. J. Med. 359, 23242336 (2008).
7. S. Achenbach, F. Moselewski, D. Ropers, M. Ferencik, U. Hoffmann, B. MacNeill, K. Pohle, U. Baum, K. Anders, I. K. Jang, W. G. Daniel, and T. J. Brady, “Detection of calcified and noncalcified coronary atherosclerotic plaque by contrast-enhanced, submillimeter multidetector spiral computed tomography: A segment-based comparison with intravascular ultrasound,” Circulation 109, 1417 (2004).
8. A. W. Leber, A. Becker, A. Knez, F. von Ziegler, M. Sirol, K. Nikolaou, B. Ohnesorge, Z. A. Fayad, C. R. Becker, M. Reiser, G. Steinbeck, and P. Boekstegers, “Accuracy of 64-slice computed tomography to classify and quantify plaque volumes in the proximal coronary system: A comparative study using intravascular ultrasound,” J. Am. Coll. Cardiol. 47, 672677 (2006).
9. M. Petranovic, A. Soni, H. Bezzera, R. Loureiro, A. Sarwar, C. Raffel, E. Pomerantsev, I. K. Jang, T. J. Brady, S. Achenbach, and R. C. Cury, “Assessment of nonstenotic coronary lesions by 64-slice multidetector computed tomography in comparison to intravascular ultrasound: Evaluation of nonculprit coronary lesions,” J. Cardiovasc. Comput. Tomogr. 3, 2431 (2009).
10. T. Deschamps and L. D. Cohen, “Fast extraction of minimal paths in 3D images and applications to virtual endoscopy,” Med. Image Anal. 5, 281299 (2001).
11. L. M. Lorigo, O. D. Faugeras, W. E. Grimson, R. Keriven, R. Kikinis, A. Nabavi, and C. F. Westin, “CURVES: Curve evolution for vessel segmentation,” Med. Image Anal. 5, 195206 (2001).
12. R. Manniesing, M. Schaap, S. Rozie, R. Hameeteman, D. Vukadinovic, A. van der Lugt, and W. Niessen, “Robust CTA lumen segmentation of the atherosclerotic carotid artery bifurcation in a large patient population,” Med. Image Anal. 14, 759769 (2010).
13. C. T. Metz, M. Schaap, A. C. Weustink, N. R. Mollet, T. van Walsum, and W. J. Niessen, “Coronary centerline extraction from CT coronary angiography images using a minimum cost path approach,” Med. Phys. 36, 55685579 (2009).
14. M. Schaap, L. Neefjes, C. Metz, A. van der Giessen, A. Weustink, N. Mollet, J. Wentzel, T. van Walsum, and W. Niessen, “Coronary lumen segmentation using graph cuts and robust kernel regression,” in Proc. Information Processing in Medical Imaging, Vol. 5636 (Springer, 2009), pp. 528539.
15. F. Pugliese, M. G. Hunink, K. Gruszczynska, F. Alberghina, R. Malago, N. van Pelt, N. R. Mollet, F. Cademartiri, A. C. Weustink, W. B. Meijboom, C. L. Witteman, P. J. de Feyter, and G. P. Krestin, “Learning curve for coronary CT angiography: What constitutes sufficient training?,” Radiology 251, 359368 (2009).
16. I. C. Sluimer, P. F. van Waes, M. A. Viergever, and B. van Ginneken, “Computer-aided diagnosis in high resolution CT of the lungs,” Med. Phys. 30, 30813090 (2003).
17. L. Sorensen, S. B. Shaker, and M. de Bruijne, “Quantitative analysis of pulmonary emphysema using local binary patterns,” IEEE Trans. Med. Imaging 29, 559569 (2010).
18. T. Stavngaard, S. B. Shaker, K. S. Bach, B. C. Stoel, and A. Dirksen, “Quantitative assessment of regional emphysema distribution in patients with chronic obstructive pulmonary disease (COPD),” Acta Radiol. 47, 914921 (2006).
19. R. Uppaluri, E. A. Hoffman, M. Sonka, P. G. Hartley, G. W. Hunninghake, and G. McLennan, “Computer recognition of regional lung disease patterns,” Am. J. Respir. Crit. Care Med. 160(2), 648654 (1999).
20. D. Wormanns, M. Fiebich, M. Saidi, S. Diederich, and W. Heindel, “Automatic detection of pulmonary nodules at spiral CT: Clinical application of a computer-aided diagnosis system,” Eur. Radiol. 12, 10521057 (2002).
21. S. B. Gokturk, C. Tomasi, B. Acar, C. F. Beaulieu, D. S. Paik, R. B. Jeffrey Jr., J. Yee, and S. Napel, “A statistical 3D pattern processing method for computer-aided detection of polyps in CT colonography,” IEEE Trans. Med. Imaging 20, 12511260 (2001).
22. G. Kiss, J. Van Cleynenbreugel, M. Thomeer, P. Suetens, and G. Marchal, “Computer-aided diagnosis in virtual colonography via combination of surface normal and sphere fitting methods,” Eur. Radiol. 12, 7781 (2002).
23. R. J. T. Sadleir and P. F. Whelan, “Colon centreline calculation for CT colonography using optimised 3D topological thinning,” in Proc. First International Symposium on 3D Data Processing Visualization and Transmission, 800803 (2002).
24. P. Sundaram, A. Zomorodian, C. Beaulieu, and S. Napel, “Colon polyp detection using smoothed shape operators: Preliminary results,” Med. Image Anal. 12, 99119 (2008).
25. T. W. Freer and M. J. Ulissey, “Screening mammography with computer-aided detection: Prospective study of 12,860 patients in a community breast center,” Radiology 220, 781786 (2001).
26. S. Nawano, K. Murakami, N. Moriyama, H. Kobatake, H. Takeo, and K. Shimura, “Computer-aided diagnosis in full digital mammography,” Invest. Radiol. 34, 310316 (1999).
27. R. M. Nishikawa, “Current status and future directions of computer-aided diagnosis in mammography,” Comput. Med. Imaging Graph. 31, 224235 (2007).
28. T. Tanaka, N. Nitta, S. Ohta, T. Kobayashi, A. Kano, K. Tsuchiya, Y. Murakami, S. Kitahara, M. Wakamiya, A. Furukawa, M. Takahashi, and K. Murata, “Evaluation of computer-aided detection of lesions in mammograms obtained with a digital phase-contrast mammography system,” Eur. Radiol. 19, 28862895 (2009).
29. Y. Ge, D. R. Stelts, J. Wang, and D. J. Vining, “Computing the centerline of a colon: A robust and efficient method based on 3D skeletons,” J. Comput. Assist. Tomogr. 23, 786794 (1999).
30. C. M. Ma and M. Sonka, “A fully parallel 3D thinning algorithm and its applications,” Comput. Vis. Image Underst. 64, 420433 (1996).
31. C. M. Ma, S. Y. Wan, and J. D. Lee, “Three-dimensional topology preserving reduction on the 4-subfields,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 15941605 (2002).
32. S. R. Aylward and E. Bullitt, “Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction,” IEEE Trans. Med. Imaging 21, 6175 (2002).
33. O. Wink, W. J. Niessen, and M. A. Viergever, “Fast delineation and visualization of vessels in 3D angiographic images,” IEEE Trans. Med. Imaging 19, 337346 (2000).
34. O. Wink, W. J. Niessen, and M. A. Viergever, “Multiscale vessel tracking,” IEEE Trans. Med. Imaging 23, 130133 (2004).
35. L. D. Cohen and R. Kimmel, “Global minimum for active contour models: A minimal path approach,” Int. J. Comput. Vision 24, 5778 (1997).
36. I. Bitter, A. E. Kaufman, and M. Sato, “Penalized-distance volumetric skeleton algorithm,” IEEE Trans. Vis. Comput. Graph. 7, 195206 (2001).
37. T. Boskamp, D. Rinck, F. Link, B. Kummerlen, G. Stamm, and P. Mildenberger, “New vessel analysis tool for morphometric quantification and visualization of vessels in CT and MR imaging datasets,” Radiographics 24, 287297 (2004).
38. D. Q. Chen, B. Li, Z. R. Liang, M. Wan, A. Kaufman, and M. Wax, “A tree-branch searching, multiresolution approach to skeletonization for virtual endoscopy,” Med. Imaging Image Process 1(Parts 1 and 2), 726734 (2000).
39. Y. Zhou and A. W. Toga, “Efficient skeletonization of volumetric objects,” IEEE Trans. Vis. Comput. Graph. 5, 196209 (1999).
40. E. Arnoldi, M. Gebregziabher, U. J. Schoepf, R. Goldenberg, L. Ramos-Duran, P. L. Zwerner, K. Nikolaou, M. F. Reiser, P. Costello, and C. Thilo, “Automated computer-aided stenosis detection at coronary CT angiography: Initial experience,” Eur. Radiol. 20, 11601167 (2010).
41. M. S. Dinesh, P. Devarakota, and J. Kumar, “Automatic detection of plaques with severe stenosis in coronary vessels of CT angiography,” in Proc. SPIE Medical Imaging, Vol. 7624, Medical Imaging 2010: Computer-Aided Diagnosis, pp. 76242Q.
42. E. J. Halpern and D. J. Halpern, “Diagnosis of coronary stenosis with CT angiography comparison of automated computer diagnosis with expert readings,” Acad. Radiol. 18, 324333 (2011).
43. B. M. Kelm, S. Mittal, Y. Zheng, A. Tsymbal, D. Bernhardt, F. Vega-Higuera, S. K. Zhou, P. Meer, and D. Comaniciu, “Detection, grading and classification of coronary stenoses in computed tomography angiography,” Med. Image. Comput. Comput. Assist. Interv. 14, 2532 (2011).
44. R. Goldenberg, D. Eilot, G. Begelman, E. Walach, E. Ben-Ishai, and N. Peled, “Computer-aided simple triage (CAST) for coronary CT angiography (CCTA),” Int. J. Comput. Assist. Radiol. Surg. 7(6), 819827 (2012).
45. T. S. Kristensen, K. F. Kofoed, J. T. Kuhl, W. B. Nielsen, M. B. Nielsen, and H. Kelbaek, “Prognostic implications of nonobstructive coronary plaques in patients with non-ST-segment elevation myocardial infarction: A multidetector computed tomography study,” J. Am. Coll. Cardiol. 58, 502509 (2011).
46. G. W. Stone, A. Maehara, A. J. Lansky, B. de Bruyne, E. Cristea, G. S. Mintz, R. Mehran, J. McPherson, N. Farhat, S. P. Marso, H. Parise, B. Templin, R. White, Z. Zhang, and P. W. Serruys, “A prospective natural-history study of coronary atherosclerosis,” N. Engl. J. Med. 364, 226235 (2011).
47. D. Kang, P. J. Slomka, R. Nakazato, V. Y. Cheng, J. K. Min, D. Li, D. S. Berman, C.-C. J. Kuo, and D. Dey, “Automatic detection of significant and subtle arterial lesions from Coronary CT Angiography,” in Proc. SPIE Medical Imaging, Vol. 8314, Medical Imaging 2012: Image Processing, pp. 831435.
48. D. Dey, V. Y. Cheng, P. J. Slomka, R. Nakazato, A. Ramesh, S. Gurudevan, G. Germano, and D. S. Berman, “Automated 3-dimensional quantification of noncalcified and calcified coronary plaque from coronary CT angiography,” J. Cardiovasc. Comput. Tomogr. 3, 372382 (2009).
49. D. Dey, T. Schepis, M. Marwan, P. J. Slomka, D. S. Berman, and S. Achenbach, “Automated three-dimensional quantification of noncalcified coronary plaque from coronary CT angiography: Comparison with intravascular US,” Radiology 257, 516522 (2010).
50. G. Yang, P. Kitslaar, M. Frenay, A. Broersen, M. J. Boogers, J. J. Bax, J. H. Reiber, and J. Dijkstra, “Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography,” in International Journal of Cardiovascular Imaging (Springer, Netherlands, 2011), pp. 113.
51. F. Cademartiri, L. La Grutta, G. Runza, A. Palumbo, E. Maffei, N. R. Mollet, T. V. Bartolotta, P. Somers, M. Knaapen, S. Verheye, M. Midiri, R. Hamers, and N. Bruining, “Influence of convolution filtering on coronary plaque attenuation values: Observations in an ex vivo model of multislice computed tomography coronary angiography,” Eur. Radiol. 17, 18421849 (2007).
52. S. Achenbach, “Cardiac CT: State of the art for the detection of coronary arterial stenosis,” J. Cardiovasc. Comput. Tomogr. 1, 320 (2007).
53. T.-C. Lee, R. L. Kashyap, and C.-N. Chu, “Building skeleton models via 3-D medial surface/axis thinning algorithms,” Graph. Models Image Process. 56, 462478 (1994).
54. E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numer. Math. 1, 269271 (1959).
55. F. Cademartiri, N. R. Mollet, G. Runza, N. Bruining, R. Hamers, P. Somers, M. Knaapen, S. Verheye, M. Midiri, G. P. Krestin, and P. J. de Feyter, “Influence of intracoronary attenuation on coronary plaque measurements using multislice computed tomography: Observations in an ex vivo model of coronary computed tomography angiography,” Eur. Radiol. 15, 14261431 (2005).
56. S. S. Halliburton, P. Schoenhagen, A. Nair, A. Stillman, M. Lieber, E. M. Tuzcu, D. G. Vince, and R. D. White, “Contrast enhancement of coronary atherosclerotic plaque: A high-resolution, multidetector-row computed tomography study of pressure-perfused, human ex-vivo coronary arteries,” Coron Artery Dis. 17, 553560 (2006).
57. S. Achenbach, K. Boehmer, T. Pflederer, D. Ropers, M. Seltmann, M. Lell, K. Anders, A. Kuettner, M. Uder, W. G. Daniel, and M. Marwan, “Influence of slice thickness and reconstruction kernel on the computed tomographic attenuation of coronary atherosclerotic plaque,” J. Cardiovasc. Comput. Tomogr. 4, 110115 (2010).
58. S. Achenbach, “Computed tomography coronary angiography,” J. Am. Coll. Cardiol. 48, 19191928 (2006).
59. V. Cheng, A. Gutstein, A. Wolak, Y. Suzuki, D. Dey, H. Gransar, L. E. Thomson, S. W. Hayes, J. D. Friedman, and D. S. Berman, “Moving beyond binary grading of coronary arterial stenoses on coronary computed tomographic angiography: Insights for the imager and referring clinician,” JACC: Cardiovasc. Imaging 1, 460471 (2008).
60. J. T. Dodge Jr., B. G. Brown, E. L. Bolson, and H. T. Dodge, “Intrathoracic spatial location of specified coronary segments on the normal human heart. Applications in quantitative arteriography, assessment of regional risk and contraction, and anatomic display,” Circulation 78, 11671180 (1988).
61. G. L. Raff, A. Abidov, S. Achenbach, D. S. Berman, L. M. Boxt, M. J. Budoff, V. Cheng, T. DeFrance, J. C. Hellinger, and R. P. Karlsberg, “SCCT guidelines for the interpretation and reporting of coronary computed tomographic angiography,” J. Cardiovasc. Comput. Tomogr. 3, 122136 (2009).
62.Analyse-it for Microsoft Excel (version 2.20),” Analyse-it Software, Ltd., see (2009).
63. P. A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach (Prentice-Hall, 1982).
64. J. H. Reiber, P. W. Serruys, C. J. Kooijman, W. Wijns, C. J. Slager, J. J. Gerbrands, J. C. Schuurbiers, A. den Boer, and P. G. Hugenholtz, “Assessment of short-, medium-, and long-term variations in arterial dimensions from computer-assisted quantitation of coronary cineangiograms,” Circulation 71, 280288 (1985).
65. A. S. Agatston, W. R. Janowitz, F. J. Hildner, N. R. Zusmer, M. Viamonte Jr., and R. Detrano, “Quantification of coronary artery calcium using ultrafast computed tomography,” J. Am. Coll. Cardiol. 15, 827832 (1990).
66. T. Q. Callister, B. Cooil, S. P. Raya, N. J. Lippolis, D. J. Russo, and P. Raggi, “Coronary artery disease: Improved reproducibility of calcium scoring with an electron-beam CT volumetric method,” Radiology 208(3), 807814 (1998).

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Visual analysis of three-dimensional (3D) coronary computed tomography angiography (CCTA) remains challenging due to large number of image slices and tortuous character of the vessels. The authors aimed to develop a robust, automated algorithm for unsupervised computer detection of coronary artery lesions.


The authors’ knowledge-based algorithm consists of centerline extraction, vessel classification, vessel linearization, lumen segmentation with scan-specific lumen attenuation ranges, and lesion location detection. Presence and location of lesions are identified using a multi-pass algorithm which considers expected or “normal” vessel tapering and luminal stenosis from the segmented vessel. Expected luminal diameter is derived from the scan by automated piecewise least squares line fitting over proximal and mid segments (67%) of the coronary artery considering the locations of the small branches attached to the main coronary arteries.


The authors applied this algorithm to 42 CCTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis ≥25%. The reference standard was provided by visual and quantitative identification of lesions with any stenosis ≥25% by three expert readers using consensus reading. The authors algorithm identified 42 lesions (93%) confirmed by the expert readers. There were 46 additional lesions detected; 23 out of 39 (59%) of these were less-stenosed lesions. When the artery was divided into 15 coronary segments according to standard cardiology reporting guidelines, per-segment basis, sensitivity was 93% and per-segment specificity was 81% using 10-fold cross-validation.


The authors’ algorithm shows promising results in the detection of both obstructive and nonobstructive CCTA lesions.


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