Adaptive kernels for multi-fiber reconstruction

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5636 LNCS, Issue , 2009, Pages 338-349

  Barmpoutis, Angelos ^a   Jian, Bing ^b   Vemuri, Baba C ^a  

^a UNIVERSITY OF FLORIDA   (United States)

^b SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE KERNELS; ADAPTIVE MODELS; DATA SETS; DIFFUSION-WEIGHTED MRI; EXISTING METHOD; NON-TRIVIAL; NOVEL METHODS; SPHERICAL DECONVOLUTION; THEORETIC LIMIT; UNKNOWN PARAMETERS;

CONVOLUTION; DATA PROCESSING; SPHERES;

MEDICAL IMAGING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; DIFFUSION WEIGHTED IMAGING; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; MYELINATED NERVE; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; ULTRASTRUCTURE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; DIFFUSION MAGNETIC RESONANCE IMAGING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; NERVE FIBERS, MYELINATED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 70349099240     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02498-6_28     Document Type: Conference Paper

References (27)

1. Le Bihan, D., et al.: MR imaging of intravoxel incoherent motions: Application to diffusion and perfusion in neurologic disorders. Radiology 161, 401-407 (1986)
2. Basser, P.J., et al.: Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. B 103, 247-254 (1994)
3. Frank, L.: Characterization of Anisotropy in High Angular Resolution Diffusion Weighted MRI. Magn. Reson. Med. 47(6), 1083-1099 (2002)
4. Alexander, D.C., et al.: Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain. MRM 48(2), 331-340 (2002)
5. von dem Hagen, E.A.H., Henkelman, R.M.: Orientational diffusion reflects fiber structure within a voxel. Magn. Reson. Med. 48(3), 454-459 (2002)
6. Tuch, D.S., et al.: High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. MRM 48(4), 577-582 (2002)
7. Barmpoutis, A., et al.: Symmetric positive 4th order tensors and their estimation from diffusion weighted MRI. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol.4584, pp. 308-319. Springer, Heidelberg (2007)
8. Ozarslan̈ E., et al.: Resolution of complex tissue microarchitecture using the diffusion orientation transform. NeuroImage 36(3), 1086-1103 (2006)
9. Zhan, W., Yang, Y.: How accurately can the diffusion profiles indicate multiple fiber orientations? a study on general fiber crossings in diffusion MRI. Journal of Magnetic Resonance 183(2), 193-202 (2006)
10. Wedeen, V.J., et al.: Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. MRM 54(6), 1377-1386 (2005)
11. Tuch, D.S.: Q-ball imaging. Magn. Reson. Med. 52(6), 1358-1372 (2004)
12. Anderson, A.W.: Measurement of fiber orientation distributions using high angular resolution diffusion imaging. MRM 54(5), 1194-1206 (2005)
13. Hess, C.P., et al.: Q-ball reconstruction of multimodal fiber orientations using the spherical harmonic basis. MRM 56(1), 104-117 (2006)
14. Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: Regularized, fast and robust analytical q-ball imaging. MRM 58, 497-510 (2007)
15. Behrens, T., et al.: Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. 50(2), 1077-1088 (2003)
16. Assaf, Y., et al.: New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter. MRM 52(5), 965-978 (2004)
17. Hosey, T.P., et al.: Inference of multiple fiber orientations in high angular resolution diffusion imaging. MRM 54(6), 1480-1489 (2005)
18. Behrens, T., et al.: Probabilistic tractography with multiple fibre orientations: What can we gain? NeuroImage 34, 144-155 (2007)
19. Tournier, J.D., Calamante, F., Gadian, D.G., Connelly, A.: Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage 23(3), 1176-1185 (2004)
20. Jian, B., et al.: A novel tensor distribution model for the diffusion weighted MR signal. NeuroImage 37(1), 164-176 (2007)
21. Ramirez-Manzanares, A., et al.: Diffusion basis functions decomposition for estimating white matter intravoxel fiber geometry. IEEE Trans. Med. Imaging 26(8), 1091-1102 (2007)
22. Kumar, R., et al.: Multi-fiber reconstruction from DW-MRI using a continuous mixture of von mises-fisher distributions. In: MMBIA (2008)
23. Rathi, Y., Michailovich, O., Bouix, S., Shenton, M.: Directional functions for orientation distribution estimation. In: ISBI, pp. 927-930 (2008)
24. Jian, B., Vemuri, B.C.: A unified computational framework for deconvolution to reconstruct multiple fibers from diffusion weighted MRI. IEEE Trans. Med. Imaging 26(11), 1464-1471 (2007)
25. S̈oderman, O., J̈onsson, B.: Restricted diffusion in cylindirical geometry. J. Magn. Reson. B 117(1), 94-97 (1995)
26. Lawson, C.L., Hanson, R.J.: Solving Least Squares Problems. Prentice-Hall, En-glewood Cliffs (1974)
27. Shepherd, T.M., et al.: Structural insights from high-resolution diffusion tensor imaging and tractography of the isolated rat hippocampus. NeuroImage 32(4), 1499-1509 (2006)