Semiautomated analysis of carotid artery wall thickness in MRI

Journal of Magnetic Resonance Imaging

Volumn 39, Issue 6, 2014, Pages 1457-1467

  Saba, Luca ^a   Gao, Hao ^b   Raz, Eytan ^c   Sree, S Vinitha ^d   Mannelli, Lorenzo ^e   Tallapally, Niranjan ^f   Molinari, Filippo ^g   Bassareo, Pier Paolo ^a   Acharya, U Rajendra ^h   Poppert, Holger ⁱ   Suri, Jasjit S ^j,k  

^a DEPARTMENT OF RADIOLOGY   (Italy)

^b UNIVERSITY OF STRATHCLYDE   (United Kingdom)

^c NEW YORK UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d Global Biomedical Technologies   (United States)

^e UNIVERSITY OF WASHINGTON   (United States)

^f Biolab Department of Electronics Politecnico di Torino ^*  (United States)

^g POLITECNICO DI TORINO   (Italy)

^h DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

ⁱ TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^j AtheroPoint LLC   (United States)

^k IDAHO STATE UNIVERSITY   (United States)

more..

Author keywords

carotid arterial wall thickness; level set method; plaque; segmentation

Indexed keywords

ARTERIAL WALL THICKNESS; ARTICLE; ATHEROSCLEROSIS; ATHEROSCLEROTIC PLAQUE; AUTOANALYSIS; CAROTID ARTERY; CLINICAL ARTICLE; COMPUTER ANALYSIS; DATA ANALYSIS; HUMAN; MEASUREMENT; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; AGED; FEMALE; IMAGE PROCESSING; MALE; OBSERVER VARIATION; PATHOLOGY; PROCEDURES; REPRODUCIBILITY;

AGED; CAROTID ARTERIES; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; OBSERVER VARIATION; REPRODUCIBILITY OF RESULTS;

EID: 84900027066     PISSN: 10531807     EISSN: 15222586     Source Type: Journal    
DOI: 10.1002/jmri.24307     Document Type: Article

References (27)

1. Saba L, Lai ML, Montisci R, et al. Association between carotid plaque enhancement shown by multidetector CT angiography and histologically validated microvessel density. Eur Radiol 2012; 22: 2237-2245.
2. Fayad ZA, the assessment of the vulnerable atherosclerotic plaque using MR imaging: a brief review. Int J Cardiac Imaging 2001; 17: 165-177.
3. Li AE, Kamel I, Rando F, et al. Using MRI to assess aortic wall thickness in the multiethnic study of atherosclerosis: distribution by race, sex, and age. Am J Roentgenol 2004; 182: 593-597.
4. Baldassarre D, Amato M, Pustina L, et al. Measurement of carotid artery intima-media thickness in dyslipidemic patients increases the power of traditional risk factors to predict cardiovascular events. Atherosclerosis 2007; 191: 403-408.
5. Suri JS, Liu K, Singh S, et al. Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review. IEEE Trans Inf Technol Biomed 2002; 6: 8-28.
6. Ladak HM, Thomas JB, Mitchell JR, et al. A semi-automatic technique for measurement of arterial wall from black blood MRI. Med Phys 2001; 28: 1098-1107.
7. Underhill HR, Kerwin WS, Hatsukami TS, Yuan C,. Automated measurement of mean wall thickness in the common carotid artery by MRI: a comparison to intima-media thickness by B-mode ultrasound. J Magn Reson Imaging 2006; 24: 379-387.
8. Adame IM, van der Geest RJ, Wasserman BA, et al. Automatic segmentation and plaque characterization in atherosclerotic carotid artery MR images. Magma 2004; 16: 227-234.
9. Liu F, Xu D, Ferguson MS, Chu B, et al. Automated in vivo segmentation of carotid plaque MRI with morphology-enhanced probability maps. Magn Reson Med 2006; 55: 659-668.
10. Yuan C, Lin E, Millard J, Hwang JN,. Closed contour edge detection of blood vessel lumen and outer wall boundaries in black-blood MR images. Magn Reson Imaging 1999; 17: 257-266.
11. Tang H, van Onkelen RS, van Walsum T, et al. A semi-automatic method for segmentation of the carotid bifurcation and bifurcation angle quantification on black blood MRA. Med Image Comput Comput Assist Interv 2010; 13: 97-104.
12. Bemmel CM, Spreeuwers LJ, Viergever MA, Niessen WJ,. Level-set based carotid artery segmentation for stenosis grading, medical image computing and computer-assisted intervention-MICCAI 2002. Lecture Notes Comput Sci 2002; 2489: 36-43.
13. Kozerke S, Botnar R, Oyre S, et al. Automatic vessel segmentation using active contours in cine phase contrast flow measurements. J Magn Reson Imaging 1999; 10: 41-51.
14. Varghese A, Merrifield RD, Crowe LA, et al. Evaluation of carotid artery wall volume measurement using novel semiautomated analysis software. J Magn Reson Imaging 2006; 24: 1401-1408.
15. Li C XC, Gui C, Fox M,. Level set evolution without re-initialization: a new variational formulation. CVPR 2005.
16. Xu C, Prince JL,. Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 1998; 7: 359-369.
17. Esposito L, Saam T, Heider P, et al. MRI plaque imaging reveals high-risk carotid plaques especially in diabetic patients irrespective of the degree of stenosis. BMC Med Imaging 2010; 10: 27.-.
18. Suri JS, Haralick RM, Sheehan FH,. Greedy algorithm for error correction in automatically produced boundaries from low contrast ventriculograms. Pattern Anal Appl 2000; 3: 39-60.
19. Virmani R, Kolodgie FD, Burke AP, et al. Lessons from sudden coronary death: a comprehensive morphological classification scheme for atherosclerotic lesions. Arterioscler Thromb Vasc Biol 2000; 20: 1262-1275.
20. Gao H, Long Q, Graves M, Gillard JH, et al. Study of reproducibility of human arterial plaque reconstruction and its effects on stress analysis based on multispectral in vivo magnetic resonance imaging. J Magn Reson Imaging 2009; 30: 85-93.
21. Xu CY, Prince JL,. Generalized gradient vector flow external forces for active contours. Signal Process 1998; 7: 131-139.
22. Li CM, Liu JD, Fox MD,. Segmentation of external force field for automatic initialization and splitting of snakes. Pattern Recogn 2005; 38: 1947-1960.
23. Choi S, Kim C,. Automatic initialization active contour model for the segmentation of chest wall on chest CT. Healthc Inform Res 2010; 16: 36-45.
24. Zhu GP, Zhang SQ, Zeng QS, Wang CH,. Gradient vector flow active contours with prior directional information. Pattern Recogn Lett 2010; 31: 845-856.
25. Li B, Acton ST,. Active contour external force using vector field convolution for image segmentation. IEEE Trans Image Process 2007: 16: 2096-106.
26. Young VE, Patterson AJ, Tunnicliffe EM, et al. Signal-to-noise ratio increase in carotid atheroma MRI: a comparison of 1.5 and 3 T. Br J Radiol 2012; 85: 937-44.
27. Biasiolli L, Lindsay AC, Choudhury RP, Robson MD,. Loss of fine structure and edge sharpness in fast-spin-echo carotid wall imaging: measurements and comparison with multiple-spin-echo in normal and atherosclerotic subjects. J Magn Reson Imaging 2011; 33: 1136-1143.