From spatial regularization to anatomical priors in fMRI analysis

Lecture Notes in Computer Science

Volumn 3565, Issue , 2005, Pages 88-100

  Ou, Wanmei ^a   Golland, Polina ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTER SIMULATION; DATA ACQUISITION; MARKOV PROCESSES; SIGNAL PROCESSING; SIGNAL TO NOISE RATIO;

DETECTION ALGORITHMS; FMRI DETECTION; GAUSSIAN SMOOTHING; MARKOV RANDOM FIELDS;

MAGNETIC RESONANCE IMAGING;

EID: 26444586165     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11505730_8     Document Type: Conference Paper

References (25)

1. Brain Voyager software package, http://www.brainvoyager.de.
2. FMRIB software library, http://www.fmrib.ox.ac.uk/fsl.
3. Besag, J. Spatial interaction and statistical analysis of lattice systems. Acad. R. Statistical Soc. Series B, 36:721-741, 1974.
4. Burock, M.A., and Dale, A.M. Estimation and detection of event-related fmri signals with temporally correlated noise: A statistically efficient and unbiased approach. Human Brain Mapping, 11:249-260, 2000.
5. Cosman, E.R., Fisher, J. and Wells, W.M. Exact MAP activity detection in fMRI using a GLM with an spatial. In Proc. MICCAI'04, 2:703-710, 2004.
6. A. M. Dale, et al. Cortical Surface-Based Analysis I: Segmentation and Surface Reconstruction. NeuroImage, 9:179-194, 1999.
7. Descombes, X., Kruggel, F., and Von Cramon, D.Y. fMRI signal restoration using a spatio-temporal Markov random field preserving transitions. NeuroImage, 8:340-349, 1998.
8. Descombes, X., Kruggel, F. and Von Cramon, D.Y. Spatio-temporal fMRI analysis using Markov random fields. IEEE TMI, 17(6):1028-1039, 1998.
9. Friston, K.J., et al. Statistical parametric maps in functional imaging: a general linear approach. Human Brain Mapping, 2:189-210, 1995.
10. Friston, K.J., et al. Assessing the significance of local activations using their spatial extent. Human Brain Mapping, 1:210-220, 1994.
11. Geman, S. and McClure, D. Statistical methods for tomographic image reconstruction. Proc. 46th Session of ISI, 51:22-26, 1987.
12. Geman, S. and Reynolds, G. Constrained restoration and recovery of discontinuities. IEEE Trans. PAMI, 14:367-383, 1992.
13. Greig, D.M., Porteous, B.T. and Gramon, D.Y. Exact maximum a posteriori estimation for binary images. J. R. Statistical Society, 51:271-279, 1989.
14. Hartvig, N.V. A stochastic Geometry model for functional Magnetic resonance images Scandinavian Journal of Statistics, 29:333-253, 2002.
15. Hojen-Sorensen, F., Hansen, L.K. and Rasmussen, C.E. Bayesian modeling of fMRI time series. Adv. Neuroinform. Processing Syst., Vol. 12:754-760, 2000.
16. Kapur, T., et al. Enhanced spatial priors for segmentation of magnetic resonance imagery. Proc. MICCAI'98, 148-157, 1998.
17. Langan, D.A., et al. Use of the mean-field approximation in an EM-based approach to unsupervised stochastic model-based image segmentation. Proc. ICASSP, 3:57-60, 1992.
18. G. Parisi. Statistical Field Theory. Addison-Wesley, 1998.
19. Pohl, K.M., et al. Anatomical guided segmentation with non-stationary tissue class distributions in an expectation-maximization framework. Proc. IEEE ISBI, 81-84, 2004.
20. Pohl, K.M., et al. Incorporating non-rigid registration into expectation maximization algorithm to segment MR images. Proc. MICCAI'02, 508-515, 2002.
21. Rajapakse, J.C. and Piyaratna, J. Bayesian modeling of fMRI time series. IEEE Transactions on Biomedical Engineering, 48:1186-1194, 2001.
22. Salli, E., et al. Contextual clustering for analysis of functional MRI data. IEEE TMI, 20:403-413, 2001.
23. Wei, X., et al. Functional MRI of auditory verbal working memory: long-term reproducibility analysis. NeuroImage, 21:1000-1008, 2004.
24. Woolrich, M.W., et al. Fully Bayesian spatio-temporal modeling of fMRI data. IEEE TMI, 23(2):213-231, 2004.
25. Zhang, J. The mean-field theory in EM procedures for markov random field. IEEE Trans. on Signal Processing, 40:2570-2583, 1992.