O Nestares | PubMed Health

PubMed2000

Robust multiresolution alignment of MRI brain volumes.

An algorithm for the automatic alignment of MRI volumes of the human brain was developed, based on techniques adopted from the computer vision literature for image motion estimation. Most image registration techniques rely on the assumption that corresponding voxels in the two volumes have equal intensity, which is not true for MRI volumes acquired with different coils and/or pulse sequences. Intensity normalization and contrast equalization were used to minimize the differences between the intensities of the two volumes. However, these preprocessing steps do not correct perfectly for the image differences when using different coils and/or pulse sequences. Hence, the alignment algorithm relies on robust estimation, which automatically ignores voxels where the intensities are sufficiently different in the two volumes. A multiresolution pyramid implementation enables the algorithm to estimate large displacements. The resulting algorithm is used routinely to align MRI volumes acquired using different protocols (3D SPGR and 2D fast spin echo) and different coils (surface and head) to subvoxel accuracy (better than 1 mm).

Algorithms↗

PubMed1997

Modeling the apparent frequency-specific suppression in simple cell responses.

Simple cells in cat striate cortex are selective for spatial frequency. It is widely believed that this selectivity arises simply because of the way in which the neurons sum inputs from the lateral geniculate nucleus. Alternate models, however, advocate the need for frequency-specific inhibitory mechanisms to refine the spatial frequency selectivity. Indeed, simple cell responses are often suppressed by superimposing stimuli with spatial frequencies that flank the neuron's preferred spatial frequency. In this article, we compare two models of simple cell responses head-to-head. One of these models, the flanking-suppression model, includes an inhibitory mechanism that is specific to frequencies that flank the neuron's preferred spatial frequency. The other model, the nonspecific-suppression model, includes a suppressive mechanism that is very broadly tuned for spatial frequency. Both models also include a rectification nonlinearity and both may include an additional accelerating (e.g., squaring) output nonlinearity. We demonstrate that both models can be consistent with the apparent flanking suppression. However, based on other experimental results, we argue that the nonspecific-suppression model is more plausible. We conclude that the suppression is probably broadly tuned for spatial frequency and that the apparent flanking suppression is actually due to distortions introduced by an accelerating output nonlinearity.

Animals↗