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Activity Number: 61 - Statistical Analysis of Complex-Valued MRI
Type: Topic Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #328863 Presentation
Title: Bayesian Image Analysis in Fourier Space for MRI Data
Author(s): John Kornak* and Karl Young
Companies: University of California, San Francisco and University of California, San Francisco (retired)
Keywords: Bayesian image analysis; Fourier space; Magnetic resonance imaging

Data from magnetic resonance imaging (MRI) modalities are acquired in terms of spatial frequencies, and are therefore representable in Fourier or k-space. The data are then typically inverse-Fourier-transformed, and the magnitude of the complex signal at each location is extracted to display an image. Bayesian image analysis can improve image quality, by balancing a priori expectations of image characteristics with a model for the noise process via Bayes Theorem. In contrast to conventional Bayesian image analysis, we here preform Bayesian image analysis in Fourier space (BIFS), i.e., directly applied to the raw MRI data in Fourier space. By specifying the Bayesian model in Fourier space, spatially correlated priors, that are relatively difficult to model and compute in conventional image space, can be efficiently modeled as a set of independent processes across Fourier space; the priors in Fourier space are modeled as independent, but tied together by defining a function over Fourier space for the parameters. We will describe the BIFS modeling approach specifically for MRI, and demonstrate benefits to model specification, posterior image properties, and computational efficiency.

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

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