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Activity Number: 249 - Multivariate Methods for Neuroimaging Data
Type: Contributed
Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Imaging
Abstract #323017
Title: A Full Bayesian Approach to SENSE Image Reconstruction Increases Brain Tissue Contrast and Reduces Noise for More Accurate Statistical Analysis
Author(s): Chase J Sakitis* and Daniel B Rowe and Andrew Brown
Companies: Marquette University and Marquette University and Clemson University
Keywords: Bayesian; fMRI; SENSE; reconstruction

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 Sensitivity Encoding (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 fixed “known” coil sensitivities for image reconstruction of every subsequent image. This technique utilizes least squares estimation to evaluate voxel values which can cause difficulty because our design matrix is not always positive definite. Here, we propose a Bayesian approach where prior distributions for the unaliased images, coil sensitivities, and measurement uncertainty are assessed from the a priori calibration images. The prior information is utilized to reconstruct images from the posterior. This BSENSE model accurately reconstructed a single slice image and a series of images with a smaller variance and without artifacts in simulated and experimental data in addition to higher task activation.

Authors who are presenting talks have a * after their name.

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