A Bayesian Approach to GRAPPA Parallel FMRI Image Reconstruction Increases SNR and Power of Task Detection

TL;DR

This paper introduces BGRAPPA, a Bayesian extension of GRAPPA for fMRI that improves image quality by reducing artifacts and noise, thereby increasing SNR and enhancing task detection power.

Contribution

The paper presents a novel Bayesian method for GRAPPA that incorporates prior calibration data to better estimate missing k-space information in fMRI reconstruction.

Findings

BGRAPPA reduces artifacts and noise in reconstructed images.

Increases signal-to-noise ratio (SNR) in fMRI images.

Enhances the statistical power of task detection in fMRI studies.

Abstract

In fMRI, capturing brain activation during a task is dependent on how quickly k-space arrays are obtained. Acquiring full k-space arrays, which are reconstructed into images using the inverse Fourier transform (IFT), that make up volume images can take a considerable amount of scan time. Under-sampling k-space reduces the acquisition time, but results in aliased, or "folded," images. GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) is a parallel imaging technique that yields full images from subsampled arrays of k-space. GRAPPA uses localized interpolation weights, which are estimated per-scan and fixed over time, to fill in the missing spatial frequencies of the subsampled k-space. Hence, we propose a Bayesian approach to GRAPPA (BGRAPPA) where space measurement uncertainty are assessed from the a priori calibration k-space arrays. The prior information is utilized to estimate the missing spatial frequency values from the posterior distribution and reconstruct into full field-of-view images. Our BGRAPPA technique successfully reconstructed both a simulated and experimental single slice image with less artifacts, reduced noise leading to an increased signal-to-noise ratio (SNR), and stronger power of task detection.