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» Analysis of Functional Magnetic Resonance Imaging in Python
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MICCAI
2010
Springer
13 years 5 months ago
Multi-Diffusion-Tensor Fitting via Spherical Deconvolution: A Unifying Framework
Abstract. In analyzing diffusion magnetic resonance imaging, multitensor models address the limitations of the single diffusion tensor in situations of partial voluming and fiber c...
Thomas Schultz, Carl-Fredrik Westin, Gordon L. Kin...
ISCAS
2008
IEEE
145views Hardware» more  ISCAS 2008»
14 years 1 months ago
Group learning using contrast NMF : Application to functional and structural MRI of schizophrenia
— Non-negative Matrix factorization (NMF) has increasingly been used as a tool in signal processing in the last couple of years. NMF, like independent component analysis (ICA) is...
Vamsi K. Potluru, Vince D. Calhoun
ICASSP
2008
IEEE
14 years 2 months ago
Blind deconvolution for sparse molecular imaging
This paper considers the image reconstruction problem when the original image is assumed to be sparse and when limited information of the point spread function (PSF) is available....
Kyle Herrity, Raviv Raich, Alfred O. Hero
ICA
2004
Springer
14 years 27 days ago
Unraveling Spatio-temporal Dynamics in fMRI Recordings Using Complex ICA
Abstract. Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data is commonly carried out under the assumption that each source may be represented...
Jörn Anemüller, Jeng-Ren Duann, Terrence...
EMMCVPR
1999
Springer
13 years 11 months ago
Markov Random Field Modelling of fMRI Data Using a Mean Field EM-algorithm
This paper considers the use of the EM-algorithm, combined with mean field theory, for parameter estimation in Markov random field models from unlabelled data. Special attention ...
Markus Svensén, Frithjof Kruggel, D. Yves v...