Magnetic resonance data modeling: The Bayesian analysis toolbox. (26th April 2019)

• Record Type: Journal Article
• Authors: Quirk, James D.; Bretthorst, G. Larry; Garbow, Joel R.; Ackerman, Joseph J. H.
• Is Part Of: Concepts in magnetic resonance. Volume 47A:Number 2(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-04-26
• Subjects: Bayesian probability theory -- magnetic resonance -- parameter estimates -- signal modeling; Magnetic resonance -- Periodicals; Nuclear magnetic resonance -- Periodicals; 538
• DOI: 10.1002/cmr.a.21467
• Languages: English
• ISSNs: 1546-6086

Abstract

Abstract: Bayesian probability theory provides optimal parameter estimates and robust model selection from a family of competing data models. However, widespread adoption of the Bayesian approach to the analysis of magnetic resonance and other data types has been hindered by its perceived complexity and heavy computational burden. This manuscript describes the Bayesian Analysis Toolbox, a computationally efficient, robust, and highly optimized suite of data modeling software packages based upon the precepts of Bayesian probability theory. The Toolbox is downloadable at no cost for noncommercial applications fromhttp://bayesiananalysis.wustl.edu . The Toolbox extends Bayesian‐based data analysis to a variety of real‐world data analysis problems commonly encountered in spectroscopy and imaging, with a focus on magnetic resonance‐derived data, making the power of this approach available to the non‐expert user.