Deformable models with sparsity constraints for cardiac motion analysis

Medical Image Analysis

Volumn 18, Issue 6, 2014, Pages 927-937

  Yu, Yang ^a   Zhang, Shaoting ^b   Li, Kang ^c   Metaxas, Dimitris ^a   Axel, Leon ^d  

^a RUTGERS UNIVERSITY   (United States)

^b University of North Carolina at Charlotte   (United States)

^c DEPARTMENT OF INDUSTRIAL AND SYSTEMS ENGINEERING   (United States)

^d NEW YORK UNIVERSITY   (United States)

Author keywords

Cardiac motion analysis; Compressed sensing; Deformable models; Sparse regularization

Indexed keywords

COMPRESSED SENSING; ERRORS; MAGNETIC RESONANCE IMAGING; SIGNAL RECONSTRUCTION;

CARDIAC MOTION; CARDIAC MOTION ANALYSIS; DEFORMABLE MODELS; IMAGE APPEARANCE; PRIORI KNOWLEDGE; SPARSE REGULARIZATIONS; SPARSENESS PRIORS; SPARSITY CONSTRAINTS;

DEFORMATION;

ACCURACY; ANIMAL EXPERIMENT; ANIMAL MODEL; ARTICLE; AUTOMATION; CARDIOVASCULAR MAGNETIC RESONANCE; CONTROLLED STUDY; DEFORMABLE MODEL; DIAGNOSTIC IMAGING; DISEASE MODEL; EXPERIMENTAL CARDIOVASCULAR DISEASE; HEART CYCLE; HEART DISEASE; HUMAN; IMAGE ANALYSIS; IMAGE QUALITY; MAGNETIC FIELD; MOTION ANALYSIS SYSTEM; MOUSE; NONHUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SCANNER; PRIORITY JOURNAL; TAGGED MAGNETIC RESONANCE IMAGING; ALGORITHM; ANIMAL; BIOLOGICAL MODEL; COMPUTER SIMULATION; HEART CONTRACTION; HEART LEFT VENTRICLE FUNCTION; IMAGE ENHANCEMENT; IMAGE PROCESSING; MOTION; PHYSIOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ANIMALS; COMPUTER SIMULATION; IMAGE ENHANCEMENT; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MICE; MODELS, CARDIOVASCULAR; MOTION; MYOCARDIAL CONTRACTION; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; VENTRICULAR FUNCTION, LEFT;

EID: 84901688771     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2014.03.002     Document Type: Article

References (78)

1. Arts T., Prinzen F., Delhaas T., Milles J., Rossi A., Clarysse P. Mapping displacement and deformation of the heart with local sine-wave modeling. IEEE Trans. Med. Imag. 2010, 29:1114-1123.
2. Bardinet E., Cohen L.D., Ayache N. Tracking and motion analysis of the left ventricle with deformable superquadrics. Med. Image Anal. 1996, 1:129-149.
3. Candes E., Tao T. Near-optimal signal recovery from random projections: universal encoding strategies?. IEEE Trans. Inform. Theory 2006, 52:5406-5425.
4. Candes, E., Rudelson, M., Tao, T., Vershynin, R., 2005. Error correction via linear programming. In: 46th Annual IEEE Symposium on Foundations of Computer Science, pp. 668-681.
5. Candes E., Romberg J., Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory 2006, 52:489-509.
6. Cerqueira M.D., Weissman N.J., Dilsizian V., Jacobs A.K., Kaul S., Laskey W.K., Pennell D.J., Rumberger J.A., Ryan T., Verani M.S. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: a statement for healthcare professionals from the cardiac imaging committee of the council on clinical cardiology of the American heart association. Circulation 2002, 105:539-542.
7. Chen T., Babb J., Kellman P., Axel L., Kim D. Semiautomated segmentation of myocardial contours for fast strain analysis in cine displacement-encoded MRI. IEEE Trans. Med. Imag. 2008, 27:1084-1094.
8. Chen T., Wang X., Chung S., Metaxas D., Axel L. Automated 3D motion tracking using Gabor filter bank, robust point matching, and deformable models. IEEE Trans. Med. Imag. 2010, 29:1-11.
9. Chuang J.S., Zemljic-Harpf A., Ross R.S., Frank L.R., McCulloch A.D., Omens J.H. Determination of three-dimensional ventricular strain distributions in gene-targeted mice using tagged MRI. Mag. Reson. Med. 2010, 64:1281-1288.
10. Cohen L., Cohen I. Finite-element methods for active contour models and balloons for 2-D and 3-D images. IEEE Trans. Pattern Anal. Mach. Intell. 1993, 15:1131-1147.
11. Cootes T., Taylor C., Cooper D., Graham J. Active shape models - their training and application. Comput. Vis. Image Understand. 1995, 61:38-59.
12. Davatzikos C., Tao X., Shen D. Hierarchical active shape models, using the wavelet transform. IEEE Trans. Med. Imag. 2003, 22:414-423.
13. Donoho D.L. Compressed sensing. IEEE Trans. Inform. Theory 2006, 52:1289-1306.
14. Duan Q., Angelini E.D., Herz S.L., Ingrassia C.M., Costa K.D., Holmes J.W., Homma S., Laine A.F. Region-based endocardium tracking on real-time three-dimensional ultrasound. Ultrasound Med. Biol. 2009, 35:256-265.
15. Duan Q., Angelini E.D., Laine A.F. Surface function actives. J. Vis. Commun. Image Representat. 2009, 20:478-490.
16. Duan Q., Angelini E.D., Laine A.F. Real-time segmentation by active geometric functions. Comput. Methods Program. Biomed. 2010, 98:223-230.
17. Duta N., Sonka M. Segmentation and interpretation of MR brain images: an improved active shape model. IEEE Trans. Med. Imag. 1998, 17:1049-1062.
18. Fischler M.A., Bolles R.C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 1981, 24:381-395.
19. Grant M., Boyd S. Graph implementations for nonsmooth convex programs. Lecture Notes in Control and Information Sciences 2008, 95-110. Springer-Verlag Limited. V. Blondel, S. Boyd, H. Kimura (Eds.).
20. Haber I., Metaxas D.N., Axel L. Three-dimensional motion reconstruction and analysis of the right ventricle using tagged MRI. Med. Image Anal. 2000, 4:335-355.
21. He, J., Balzano, L., Szlam, A., 2012. Incremental gradient on the grassmannian for online foreground and background separation in subsampled video. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1568-1575.
22. Hontani, H., Matsuno, T., Sawada, Y., 2012. Robust nonrigid ICP using outlier-sparsity regularization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1025-1032.
23. Huang X., Metaxas D. Metamorphs: deformable shape and appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30:1444-1459.
24. Huang X., Dione D.P., Compas C.B., Papademetris X., Lin B.A., Bregasi A., Sinusas A.J., Staib L.H., Duncan J.S. Contour tracking in echocardiographic sequences via sparse representation and dictionary learning. Med. Image Anal. 2013.
25. Huang X., Dione D.P., Lin B., Bregasi A., Sinusas A.J., Duncan J.S. Segmentation of 4D echocardiography using stochastic online dictionary learning. Lecture Notes in Computer Science 2013, vol. 8151:57-65. Springer, Berlin Heidelberg. K. Mori, I. Sakuma, Y. Sato, C. Barillot, N. Navab (Eds.).
26. Hu Z., Metaxas D., Axel L. In vivo strain and stress estimation of the heart left and right ventricles from MRI images. Med. Image Anal. 2003, 7:435-444.
27. Jehan-Besson, S., Barlaud, M., Aubert, G., Faugeras, O., 2003. Shape gradients for histogram segmentation using active contours. In: IEEE International Conference on Computer Vision, vol. 1, pp. 408-415.
28. Kass M., Witkin A., Terzopoulos D. Snakes: active contour models. Int. J. Comput. Vis. 1988, 1:321-331.
29. Lekadir K., Merrifield R., Yang G.Z. Outlier detection and handling for robust 3-D active shape models search. IEEE Trans. Med. Imag. 2007, 26:212-222.
30. Liu W., Ashford M.W., Chen J., Watkins M.P., Williams T.A., Wickline S.A., Yu X. MR tagging demonstrates quantitative differences in regional ventricular wall motion in mice, rats, and men. Am. J. Physiol. Heart Circulatory Physiol. 2006, 291:2515-2521.
31. Mallat S., Zhang Z. Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 1993, 41:3397-3415.
32. McInerney, T., Terzopoulos, D., 1996. Deformable models in medical image analysis. In: Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 171 -180.
33. Metaxas, D., 1992. Physics-Based Modeling of Nonrigid Objects for Vision and Graphics. Ph.D. Thesis. University of Pennsylvania.
34. Metaxas D.N. Physics-Based Deformable Models: Applications to Computer Vision, Graphics, and Medical Imaging 1996, Kluwer Academic Publishers, Norwell, MA, USA. first ed.
35. Metaxas, D., Terzopoulos, D., 1991. Constrained deformable superquadrics and nonrigid motion tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 337-343.
36. Metaxas D., Terzopoulos D. Shape and nonrigid motion estimation through physics-based synthesis. IEEE Trans. Pattern Anal. Mach. Intell. 1993, 15:580-591.
37. Müller, M., Charypar, D., Gross, M., 2003. Particle-based fluid simulation for interactive applications. In: Proceedings of the 2003 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, pp. 154-159.
38. Myronenko A., Song X. Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 2010, 32:2262-2275.
39. Nealen A., Igarashi T., Sorkine O., Alexa M. Laplacian mesh optimization. Proceedings of the 4th International Conference on Computer Graphics and Interactive Techniques in Australasia and Southeast Asia 2006, 381-389. ACM, New York, NY, USA.
40. Nealen A., Muller M., Keiser R., Boxerman E., Carlson M. Physically based deformable models in computer graphics. Comput. Graph. Forum 2006, 25:809-836.
41. Osman N.F., McVeigh E.R., Prince J.L. Imaging heart motion using harmonic phase MRI. IEEE Trans. Med. Imag. 2000, 19:186-202.
42. Paragios N. A variational approach for the segmentation of the left ventricle in cardiac image analysis. Int. J. Comput. Vis. 2002, 50:345-362.
43. Paragios N. A level set approach for shape-driven segmentation and tracking of the left ventricle. IEEE Trans. Med. Imag. 2003, 22:773-776.
44. Paragios N., Deriche R. Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22:266-280.
45. Park J., Metaxas D., Axel L. Analysis of left ventricular wall motion based on volumetric deformable models and MRI-SPAMM. Med. Image Anal. 1996, 1:53-71.
46. Prastawa M., Bullitt E., Ho S., Gerig G. A brain tumor segmentation framework based on outlier detection. Med. Image Anal. 2004, 8:275-283.
47. Qian, Z., Montillo, A., Metaxas, D., Axel, L., 2003. Segmenting cardiac MRI tagging lines using Gabor filter banks. In: Proceedings of the IEEE Engineering in Medicine and Biology Society, pp. 630-633.
48. Rogers M., Graham J. Robust active shape model search. Lecture Notes in Computer Science 2006, vol. 2353:289-312. Springer, Berlin/Heidelberg. A. Heyden, G. Sparr, M. Nielsen, P. Johansen (Eds.).
49. Rueckert D., Sonoda L., Hayes C., Hill D., Leach M., Hawkes D. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imag. 1999, 18:712-721.
50. Shen D., Davatzikos C. HAMMER: hierarchical attribute matching mechanism for elastic registration. IEEE Trans. Med. Imag. 2002, 21:1421-1439.
51. Shen, T., Huang, X., Li, H., Kim, E., Zhang, S., Huang, J., 2011. A 3D Laplacian-driven parametric deformable model. In: IEEE International Conference on Computer Vision, pp. 279-286.
52. Shiee N., Bazin P.L., Cuzzocreo J.L., Blitz A., Pham D.L. Segmentation of brain images using adaptive atlases with application to ventriculomegaly. Information Processing in Medical Imaging 2011, 1-12. Springer.
53. Sorkine, O., Alexa, M., 2007. As-rigid-as-possible surface modeling. In: Proceedings of the Fifth Eurographics Symposium on Geometry Processing, Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, pp. 109-116.
54. Sorkine O., Cohen-Or D., Lipman Y., Alexa M., Rössl C., Seidel H.P. Laplacian surface editing. Proceedings of the 2004 Eurographics/ACM SIGGRAPH Symposium on Geometry Processing 2004, 175-184. ACM.
55. Tavakoli V., Amini A.A. A survey of shaped-based registration and segmentation techniques for cardiac images. Comput. Vis. Image Understand. 2013, 117:966-989.
56. Terzopoulos, D., Metaxas, D., 1990. Dynamic 3D models with local and global deformations: Deformable superquadrics. In: International Conference on Computer Vision, pp. 606-615.
57. Terzopoulos D., Metaxas D. Dynamic 3D models with local and global deformations: deformable superquadrics. IEEE Trans. Pattern Anal. Mach. Intell. 1991, 13:703-714.
58. Terzopoulos D., Witkin A., Kass M. Constraints on deformable models: recovering 3D shape and nonrigid motion. Artif. Intell. 1988, 36:91-123.
59. Tobon-Gomez C., Craene M.D., McLeod K., Tautz L., Shi W., Hennemuth A., Prakosa A., Wang H., Carr-White G., Kapetanakis S., Lutz A., Rasche V., Schaeffter T., Butakoff C., Friman O., Mansi T., Sermesant M., Zhuang X., Ourselin S., Peitgen H.O., Pennec X., Razavi R., Rueckert D., Frangi A., Rhode K. Benchmarking framework for myocardial tracking and deformation algorithms: an open access database. Med. Image Anal. 2013, 17:632-648.
60. Tropp J., Gilbert A. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inform. Theory 2007, 53:4655-4666.
61. Tustison N., Amini A. Biventricular myocardial strains via nonrigid registration of AnFigatomical NURBS models. IEEE Trans. Med. Imag. 2006, 25:94-112.
62. van Assen H.C., Danilouchkine M.G., Frangi A.F., Ord¢s S., Westenberg J.J., Reiber J.H., Lelieveldt B.P. SPASM: A 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data. Med. Image Anal. 2006, 10:286-303.
63. Wang H., Amini A.A. Cardiac motion and deformation recovery from MRI: a review. IEEE Trans. Med. Imag. 2012, 31:487-503.
64. Wang X., Chen T., Zhang S., Metaxas D., Axel L. LV motion and strain computation from tMRI based on meshless deformable models. Lecture Notes in Computer Science 2008, vol. 5241:636-644. Springer, Berlin/Heidelberg.
65. Wang, X., Metaxas, D., Chen, T., Axel, L., 2008b. Meshless deformable models for LV motion analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8.
66. Wright J., Yang A., Ganesh A., Sastry S., Ma Y. Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31:210-227.
67. Yan P., Sinusas A., Duncan J.S. Boundary element method-based regularization for recovering of LV deformation. Med. Image Anal. 2007, 11:540-554.
68. Young A., Kraitchman D., Dougherty L., Axel L. Tracking and finite element analysis of stripe deformation in magnetic resonance tagging. IEEE Trans. Med. Imag. 1995, 14:413-421.
69. Young A.A., French B.A., Yang Z., Cowan B.R., Gilson W.D., Berr S.S., Kramer C.M., Epstein F.H. Reperfused myocardial infarction in mice: 3D mapping of late gadolinium enhancement and strain. J. Cardiovasc. Mag. Reson. 2006, 8:685-692.
70. Yu Y., Zhang S., Huang J., Metaxas D., Axel L. Sparse deformable models with application to cardiac motion analysis. Information Processing in Medical Imaging 2013, 208-219. Springer.
71. Zhang, S., Wang, X., Metaxas, D., Chen, T., Axel, L., 2009. LV surface reconstruction from sparse tMRI using Laplacian surface deformation and optimization. In: International Symposium on Biomedical Imaging: From Nano to Macro, pp. 698-701.
72. Zhang S., Zhan Y., Dewan M., Huang J., Metaxas D.N., Zhou X.S. Towards robust and effective shape modeling: sparse shape composition. Med. Image Anal. 2012, 16:265-277.
73. Zhang S., Zhan Y., Metaxas D.N. Deformable segmentation via sparse representation and dictionary learning. Med. Image Anal. 2012, 16:1385-1396.
74. Zhong J., Liu W., Yu X. Characterization of three-dimensional myocardial deformation in the mouse heart: an MR tagging study. J. Mag. Reson. Imag. 2008, 27:1263-1270.
75. Zhou R., Pickup S., Glickson J.D., Scott C.H., Ferrari V.A. Assessment of global and regional myocardial function in the mouse using cine and tagged MRI. Mag. Reson. Med. 2003, 49:760-764.
76. Zhou K., Huang J., Snyder J., Liu X., Bao H., Guo B., Shum H.Y. Large mesh deformation using the volumetric graph Laplacian. ACM Trans. Graph. 2005, 24:496-503.
77. Zhu S.C., Yuille A. Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 1996, 18:884-900.
78. Zhuang X., Rhode K., Razavi R., Hawkes D., Ourselin S. A registration-based propagation framework for automatic whole heart segmentation of cardiac MRI. IEEE Trans. Med. Imag. 2010, 29:1612-1625.