✦ LIBER ✦

A data-consistent linear prediction method for image reconstruction from finite Fourier samples

✍ Scribed by Christopher P. Hess; Zhi-Pei Liang

Publisher: John Wiley and Sons
Year: 1996
Tongue: English
Weight: 588 KB
Volume: 7
Category: Article
ISSN: 0899-9457
DOI: 10.1002/(sici)1098-1098(199622)7:2<136::aid-ima10>3.0.co;2-#

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✦ Synopsis

Linear prediction (LP) methods have been widely used for highresolution spectral estimation from finite Fourier samples. Their application to image reconstruction, on the other hand, has been markedly less successful. In this article, we present an improved LP method for high-resolution image reconstruction. The distinguishing feature of the proposed method is its use of a generalized series model to enforce the data consistency constraint to compensate for reconstruction error resulting from LP modeling. Several reconstruction examples from magnetic resonance imaging data are included to demonstrate the performance of the method.

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## Abstract When the conventional Fourier transform (FT) algorithm is applied to reconstruct a magnetic resonance (MR) image from data sampled along a zig‐zag trajectory in the Fourier space, the nonuniform sampling in the spatial frequency direction may give rise to artifacts. In this paper the na