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2014 ; 4
(7
): 481-6
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Direct imaging of functional networks
#MMPMID25111798
Wong EC
Brain Connect
2014[Sep]; 4
(7
): 481-6
PMID25111798
show ga
In blood-oxygenation-level-dependent functional magnetic resonance imaging
(fMRI), current methods typically acquire ? 500,000 imaging voxels at each time
point, and then use computer algorithms to reduce this data to the coefficients
of a few hundred parcels or networks. This suggests that the amount of relevant
information present in the fMRI signal is relatively small, and presents an
opportunity to greatly improve the speed and signal to noise ratio (SNR) of the
fMRI process. In this work, a theoretical framework is presented for calculating
the coefficients of functional networks directly from highly undersampled fMRI
data. Using predefined functional parcellations or networks and a compact k-space
trajectory that samples data at optimal spatial scales, the problem of estimating
network coefficients is reformulated to allow for direct least squares
estimation, without Fourier encoding. By simulation, this approach is shown to
allow for acceleration of the imaging process under ideal circumstances by nearly
three orders of magnitude.
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