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Bayesian k-Space Estimation Decreases Image Noise and Increased Activation Detection

Abstract Number:

2490 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Dan Rowe (1)

Institutions:

(1) Marquette University, N/A

First Author:

Dan Rowe  
Marquette University

Presenting Author:

Dan Rowe  
Marquette University

Abstract Text:

In fMRI, as voxel sizes decrease, there is less material in them to produce a signal, leading to a decrease in the signal-to-noise ratio and contrast-to-noise ratio in each voxel. There have been many attempts to decrease the noise in an image in order to increase activation, but most lead to blurrier images. An alternative is to develop methods in spatial frequency space. Reducing noise in spatial frequency space has unique benefits. A Bayesian approach is proposed that quantifies available a priori information about spatial frequency coefficients, incorporates it with observed spatial frequency coefficients, and estimates spatial frequency coefficients values a posteriori. Inverse Fourier transform reconstructed images form marginal posterior mean estimated spatial frequency coefficients have reduced noise and increased detection power.

Keywords:

Bayesian|k-space|fmri|imaging| |

Sponsors:

Section on Bayesian Statistical Science

Tracks:

Applications in Applied Sciences

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