4th order diffusion tensor interpolationwith divergence and curl constrained bé zier patches

Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

Volumn , Issue , 2009, Pages 634-637

  Yassine, Inas ^a   McGraw, Tim ^a  

^a WEST VIRGINIA UNIVERSITY   (United States)

Author keywords

Biomedical image processing; Diffusion processes; Interpolation; Magnetic resonance imaging; Spline functions

Indexed keywords

BIOMEDICAL IMAGE PROCESSING; CARTESIAN TENSORS; CONTROL POINT; DIFFUSION PROCESSES; DIFFUSION TENSOR; INTERPOLATION METHOD; LINEAR INTERPOLATION; ORDER 2; PHYSICAL CONSTRAINTS; SPARSE LINEAR SYSTEMS; SPLINE FUNCTIONS; SUBDIVISION SCHEMES; TENSOR FIELDS;

DIFFUSION; IMAGE PROCESSING; IMAGING SYSTEMS; INTERPOLATION; LINEAR SYSTEMS; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; SPLINES;

TENSORS;

EID: 70449344453     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2009.5193127     Document Type: Conference Paper

References (16)

1. P.J. Basser, J. Mattiello, R. Turner, and D. Le Bihan, "Diffusion tensor echo-planar imaging of human brain", in Proceedings of the SMRM, 1993, vol. 56, pp. 584-562.
2. E. Ózarslan, B.C. Vemuri, and T.H. Mareci, "Generalized scalar measures for diffusion MRI using trace, variance, and entropy", Magnetic Resonance in Medicine, vol. 53, no. 4, pp. 866-876, 2005.
3. C. Liu, R. Bammer, B. Acar, and M. E. Moseley, "Characterizing non-Gaussian diffusion by using generalized diffusion tensors", Magnetic Resonance in Medicine, vol. 51, pp. 924-937, 2004.
4. A. Barmpoutis, B. C. Vemuri, and J. R. Forder, "Fast displacement probability profile approximation from HARDI using 4th-order tensors", IEEE International Symposium on Biomedical Imaging, pp. 911-914, 14 - 17 May 2008.
5. M. Moakher, "Fourth-order cartesian tensors: old and new facts, notions and applications", The Quarterly Journal of Mechanics and Applied Mathematics, vol. 61, no. 2, pp. 181, 2008.
6. P.T. Fletcher and S. Joshi, "Riemannian geometry for the statistical analysis of diffusion tensor data", Signal Processing, vol. 87, no. 2, pp. 250-262, 2007.
7. X. Pennec, P. Fillard, and N. Ayache, "A Riemannian Framework for Tensor Computing", International Journal of Computer Vision, vol. 66, no. 1, pp. 41-66, 2006.
8. P.G. Batchelor,M.Moakher, D. Atkinson, F. Calamante, and A. Connelly, "A rigorous framework for diffusion tensor calculus.", Magn Reson Med, vol. 53, no. 1, pp. 221-5, 2005.
9. V. Arsigny, P. Fillard, X. Pennec, and N. Ayache, "Fast and simple calculus on tensors in the Log-Euclidean framework", Proceedings of MICCAI, pp. 259-267, 2005.
10. A. Barmpoutis, B.C. Vemuri, T.M. Shepherd, and J.R. Forder, "Tensor Splines for Interpolation and Approximation of DT-MRI With Applications to Segmentation of Isolated Rat Hippocampi", Medical Imaging, IEEE Transactions on, vol. 26, no. 11, pp. 1537-1546, 2007.
11. G. Kindlmann, R. Estepar, M. Niethammer, S. Haker, and C.-F. Westin, "Geodesic-loxodromes for diffusion tensor interpolation and difference measurement", in 10th Intl. Conf. on Medical Image Computing and Computer-Assisted Intervention, Brisbane, Australia, October 2007, pp. 1-9.
12. D. Suter, "Vector splines in computer vision.", in Submitted : Australasian Workshop on Thin-plates, Sydney, 1994.
13. S. Gupta and J. Prince, "Stochastic models for div-curl optical flow methods", IEEE Signal Processing Letters, vol. 3, no. 2, pp. 32-34, 1996.
14. C.-H. Kim J.-M. Hong, J.-C. Yoon, "Divergenceconstrained moving least squares for fluid simulation", in Computer Animation and Virtual Worlds, September 2008, pp. 469-477.
15. I. Yassine and T. McGraw, "A subdivision approach to tensor field interpolation", in Workshop On Computational Diffusion MRI, 2008, pp. 117-124.
16. G. Farin, Curves and surfaces for computer aided geometric design: a practical guide, Academic Press Professional, Inc. San Diego, CA, USA, 1993.