Bayesian Magnitude and Phase Estimation of k-Space Data Enhances Reconstructed Image Quality

Music City Center 

It is well known in fMRI studies that the first three images in a time series have a much higher signal than the remainder of the time series. Many attempts to decrease noise and improve activation are applied on image data, where voxels may be spatially correlated. This requires the use of spatial modeling and often results in blurrier images. A Bayesian approach is employed on the uncorrelated k-space magnitude and phase data since the spatial frequency coefficients can be treated independently of each other. This method quantifies available a priori information about spatial frequency coefficients from the first three images in the time series. The spatial frequencies observed throughout the fMRI experiment are then incorporated and spatial frequency coefficients are estimated a posteriori using both the ICM algorithm and Gibbs sampling. Discrete inverse Fourier transform reconstructed images from posterior estimated spatial frequencies will have reduced noise and increased detection power.

Bayesian

fMRI

k-space 

Section on Statistics in Imaging