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.
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