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ABSTRACT: We consider signal estimation for functional MRI studies on multiple subjects. There are two major issues; alignment or registration of images across subjects; and using the multisubject information to capture covariance information: we discuss only the latter. Capturing this covariance information properly can lead to great improvements in statistical efficiency beyond what simple averaging can offer as well as compact description of group features.
04/2001;
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[show abstract]
[hide abstract]
ABSTRACT: We consider signal estimation for functional MRI studies on
multiple subjects. There are two major issues; alignment or registration
of images across subjects, and using the multisubject information to
capture covariance information; we discuss only the latter. Capturing
this covariance information properly can lead to great improvements in
statistical efficiency beyond what simple averaging can offer as well as
compact description of group features
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on; 02/2001 · 4.63 Impact Factor
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ABSTRACT: In the last half decade, fast methods of magnetic resonance imaging have led to the possibility, for the first time, of non-invasive dynamic brain imaging. This has led to an explosion of work in the Neurosciences. From a signal processing viewpoint the problems are those of nonlinear spatio-temporal system identification. In this paper, we develop new methods of identification using novel spatial regularization. We also develop a new model comparison technique and use that to compare our method with existing techniques on some experimental data.
IEEE Transactions on Medical Imaging 02/2001; 20(1):26-35. · 3.64 Impact Factor
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ABSTRACT: We consider a model comparison based on a new model selection
criterion. We treat fMRI as a spatio-temporal system identification
problem and compare our model fitting method based on spatial
regularization methods with the so-called statistical parametric map
technique currently popular in the fMRI literature. We illustrate our
results with data from the brain obtained during a combined visual and
motor experiment
Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on; 02/1999
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ABSTRACT: The authors consider spatio-temporal modelling of functional MRI
data from an inverse problems point of view. Most of the modelling to
date has been on a pixel by pixel basis with no acknowledgment given to
spatial smoothness when it exists. The authors discuss regularization
methods that address that issue and illustrate their results with
experimental data
Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on; 11/1998
