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Proceedings Paper

Resolution Enhancement of Magnetic Resonance Images Using an Iterative Data Refinement Technique
Author(s): D. W. Ro; G. T. Herman; P. M. Joseph
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Paper Abstract

Iterative Data Refinement (abbreviated IDR) is a general procedure which encompasses many special procedures for image reconstruction and for related problems. It is a procedure for estimating data that would have been collected by an idealized measuring device from data that were collected by an actual measuring device. Such approaches have been applied successfully in areas of reconstruction in x-ray tomographic radiology. In fact, IDR is general enough to encompass standard approaches to data recovery, such as the Error-Reduction and the Hybrid Input-Output methods. Along similar lines, IDR provides a common framework within which new algorithms can be developed for improved magnetic resonance imaging (MRI). We have applied and implemented the approach of IDR to a specific problem in MRI, namely to the correction of spatially-dependent blurs due to short local transverse relaxation (T2) values. The algorithm is designed to reconstruct T2-weighted spin density images with improved spatial resolution. The practical computational significance of using the IDR approach will be illustrated by the reconstruction of mathematical phantoms. We have found that over-relaxation of the algorithm improves computational speed by up to a factor of five.

Paper Details

Date Published: 1 November 1989
PDF: 11 pages
Proc. SPIE 1199, Visual Communications and Image Processing IV, (1 November 1989); doi: 10.1117/12.970105
Show Author Affiliations
D. W. Ro, University of Pennsylvania (United States)
G. T. Herman, University of Pennsylvania (United States)
P. M. Joseph, University of Pennsylvania (United States)

Published in SPIE Proceedings Vol. 1199:
Visual Communications and Image Processing IV
William A. Pearlman, Editor(s)

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