Abstract #300640


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JSM 2002 Abstract #300640
Activity Number: 17
Type: Contributed
Date/Time: Sunday, August 11, 2002 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section*
Abstract - #300640
Title: A Bayesian Approach to Manetic Resonance Image Reconstruction
Author(s): Vera Bulaevskaya*+ and Gary Oehlert
Affiliation(s): University of Minnesota and University of Minnesota
Address: 313 Ford Hall, 224 Church St. S.E., Minneapolis, Minnesota, 55455, U.S.A.
Keywords: penalized likelihood ; Bayes ; wavelets ; MR imaging
Abstract:

Magnetic Resonance Imaging (MRI) is widely used in medicine and other sciences. Currently, images obtained from MRI are reconstructed using the discrete inverse Fourier transform. This method has three serious shortcomings. First, it gives a highly discretized approximation to a naturally continuous function. Second, it requires the data sampling space to be an equally-spaced grid. Third, its approach to reconstruction of three-dimensional objects is two-dimensional because the image is reconstructed as a sequence of independent two-dimensional slices. We propose an alternative reconstruction method, using a penalized likelihood approach, that honors the continuous nature of the data, relaxes the requirements on the structure of the data sampling space, and makes a three-dimensional use of the data possible. This approach combines Kimeldorf-Wahba penalized likelihood methodology with computationally convenient bais functions, such as the Haar wavelets, and can be easily reformulated in a Bayesian context.


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