IEEE transactions on medical imaging
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IEEE Trans Med Imaging · Jan 2008
Classification of fMRI time series in a low-dimensional subspace with a spatial prior.
We propose a new method for detecting activation in functional magnetic resonance imaging (fMRI) data. We project the fMRI time series on a low-dimensional subspace spanned by wavelet packets in order to create projections that are as non-Gaussian as possible. ⋯ We expect activated areas that are connected, and impose a spatial prior in the form of a Markov random field. Our approach was validated with in vivo data and realistic synthetic data, where it outperformed a linear model equipped with the knowledge of the true hemodynamic response.
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Radial imaging techniques, such as projection-reconstruction (PR), are used in magnetic resonance imaging (MRI) for dynamic imaging, angiography, and short-T(2) imaging. They are robust to flow and motion, have diffuse aliasing patterns, and support short readouts and echo times. One drawback is that standard implementations do not support anisotropic field-of-view (FOV) shapes, which are used to match the imaging parameters to the object or region-of-interest. ⋯ It also makes possible new radial imaging applications that were previously unsuitable, such as imaging elongated regions or thin slabs. 2-D PR longitudinal leg images and thin-slab, single breath-hold 3-D PR abdomen images, both with isotropic resolution, demonstrate these new possibilities. No scan time to volume efficiency is lost by using anisotropic FOVs. The acquisition trajectories can be computed on a scan by scan basis.