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Abstract:
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In fMRI, capturing cognitive temporal dynamics is dependent on how quickly volume brain images are acquired. The sampling time for an array of spatial frequencies to reconstruct an image is the limiting factor in the fMRI process. Multi-coil SENSE image reconstruction is a parallel imaging technique that has greatly reduced image scan time. In SENSE image reconstruction, coil sensitivities are estimated once from a priori calibration images and used as “known” coil sensitivities for image reconstruction. Here a Bayesian approach is taken where prior distributions for the unaliased images, coil sensitivities, and uncertainty are assessed from the a priori calibration image information. The prior distribution is combined with the data likelihood to from a posterior distribution. Images and coil sensitivities are marginally estimated a posteriori using an MCMC Gibbs sampling algorithm. In addition, variability estimates and hypothesis testing is possible. This Bayesian SENSE (BSENSE) model utilizes prior image information to the reconstruct image from the posterior distribution and is applied to simulated and experimental fMRI data.
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