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Proceedings Paper

Bayesian reconstruction in synthetic magnetic resonance imaging
Author(s): Ranjan Maitra; Julian Besag
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Paper Abstract

In magnetic resonance imaging (MRI), three unobservable physical quantities are combined at the pixel level to produce the image. Control parameters can be pre-set to highlight contrast between different tissue types but the optimal values may be problem- and patient-specific and not known in advance. The aim in synthetic MRI is to estimate the underlying physical quantities from three images, taken at conventional settings, and to use these to synthesize images for arbitrary control parameters. Standard least squares methods are inadequate for this ill-conditioned inverse problem. The paper describes several forms of Bayesian reconstruction and suggests that these provide satisfactory alternatives.

Paper Details

Date Published: 22 September 1998
PDF: 9 pages
Proc. SPIE 3459, Bayesian Inference for Inverse Problems, (22 September 1998); doi: 10.1117/12.323818
Show Author Affiliations
Ranjan Maitra, Univ. of Maryland/Baltimore County (United States)
Julian Besag, Univ. of Washington (United States)

Published in SPIE Proceedings Vol. 3459:
Bayesian Inference for Inverse Problems
Ali Mohammad-Djafari, Editor(s)

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