A Bayesian Approach to Fused GRAPPA and SENSE MR Image Reconstruction

Oregon Convention Center 

In fMRI, capturing brain activity during a physical task is dependent on how quickly each volume k-space array is obtained. Acquiring the full k-space arrays can take a considerable amount of time. Under-sampling k-space reduces the acquisition time, but results in aliased, or "folded", images after applying the inverse Fourier transform (IFT). GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) and SENSitivity Encoding (SENSE) are parallel imaging techniques that yield full images from subsampled arrays of k-space. With GRAPPA operating in the spatial frequency domain and SENSE in image space, these techniques can be fused to reconstruct the subsampled k-space arrays more accurately. Here, we propose a Bayesian approach to this combined model where prior distributions for the unknown parameters are assessed from a priori k-space arrays. The prior information is utilized to estimate the missing spatial frequency values, unalias the voxel values from the posterior distribution, and reconstruct into full field-of-view images. Our Bayesian technique successfully reconstructed a simulated fMRI time series with no aliasing artifacts while decreasing temporal variation.

Bayesian

GRAPPA

fMRI

reconstruction

SENSE 

Section on Statistics in Imaging