Robust extrapolation scheme for fast estimation of 3D Ising field partition functions: Application to within-subject fMRI data analysis

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

Volumn 5761 LNCS, Issue PART 1, 2009, Pages 975-983

  Risser, Laurent ^a,b,c   Vincent, Thomas ^a,b   Ciuciu, Philippe ^a,b   Idier, Jérôme ^c  

^a DIF   (France)

^b IFR 49   (France)

^c INSTITUT DE RECHERCHE EN COMMUNICATIONS ET CYBERNÉTIQUE DE NANTES   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN ACTIVITY; BRAIN REGIONS; COMPUTATIONAL BURDEN; DATA SETS; EXTRAPOLATION METHODS; EXTRAPOLATION SCHEMES; FAST ESTIMATION; FMRI DATA; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HIDDEN STATE; INHOMOGENEITIES; JOINT DETECTION; MARKOV RANDOM FIELDS; NUMERICAL SCHEME; PARTITION FUNCTIONS; SAMPLING METHOD; SPATIAL CORRELATIONS; SPATIALLY ADAPTIVE;

APPROXIMATION THEORY; BRAIN; EXTRAPOLATION; MAGNETIC RESONANCE IMAGING; MEDICAL COMPUTING; RESONANCE; THREE DIMENSIONAL;

COMPUTATIONAL METHODS;

ALGORITHM; ARTICLE; BRAIN; BRAIN MAPPING; COMPUTER ASSISTED DIAGNOSIS; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; BRAIN; BRAIN MAPPING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 79551684968     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04268-3_120     Document Type: Conference Paper

References (8)

1. Vincent, T., Ciuciu, P., Idier, J.: Spatial mixture modelling for the joint detection-estimation of brain activity in fMRI. In: 32th Proc. IEEE ICASSP, Honolulu, Hawaii, vol. I, pp. 325-328 (2007)
2. Meng, X., Wong, W.: Simulating ratios of normalizing constants via a simple identity: a theoretical exploration. Statistica Sinica 6, 831-860 (1996) (Pubitemid 127004410)
3. Gelman, A., Meng, X.L.: Simulating normalizing constants: from importance sampling to bridge sampling to path sampling. Statistical Science 13, 163-185 (1998) (Pubitemid 128466008)
4. Fernández, S., Green, P.J.: Modelling spatially correlated data via mixtures: a Bayesian approach. J. R. Statist. Soc. B 64, 805-826 (2002)
5. Woolrich, M., Behrens, T., Beckmann, C., Smith, S.: Mixture models with adaptive spatial regularization for segmentation with an application to fMRI data. IEEE Trans. Med. Imag. 24, 1-11 (2005)
6. Makni, S., Idier, J., Vincent, T., Thirion, B., Dehaene-Lambertz, G., Ciuciu, P.: A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI. Neuroimage 41, 941-969 (2008)
7. Thirion, B., Flandin, G., Pinel, P., Roche, A., Ciuciu, P., Poline, J.B.: Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets. Hum. Brain Mapp. 27, 678-693 (2006)
8. Trillon, A., Idier, J., Peureux, P.: Unsupervised Bayesian 3D reconstruction for non-destructive evaluation using gammagraphy. In: EUSIPCO, Suisse (2008)