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Artifact Reduction in Moving-table Acquisitions Using Parallel Imaging and Multiple Averages by H.P. Fautz; M. Honal; U. Saueressig; O. Schäfer; S.A.R. Kannengiesser is a Medicine article available to read on EtoBox.

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## Abstract Two‐dimensional (2D) axial continuously‐moving‐table imaging has to deal with artifacts due to gradient nonlinearity and breathing motion, and has to provide the highest scan efficiency. Parallel imaging techniques (e.g., generalized autocalibrating partially parallel acquisition GRAPPA)) are used to reduce such artifacts and avoid ghosting artifacts. The latter occur in __T__~2~‐weighted multi‐spin‐echo (SE) acquisitions that omit an additional excitation prior to imaging scans for presaturation purposes. Multiple images are reconstructed from subdivisions of a fully sampled __k__‐space data set, each of which is acquired in a single SE train. These images are then averaged. GRAPPA coil weights are estimated without additional measurements. Compared to conventional image reconstruction, inconsistencies between different subsets of __k__‐space induce less artifacts when each __k__‐space part is reconstructed separately and the multiple images are averaged afterwards. These inconsistencies may lead to inaccurate GRAPPA coil weights using the proposed intrinsic GRAPPA calibration. It is shown that aliasing artifacts in single images are canceled out after averaging. Phant

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Author

H.P. Fautz; M. Honal; U. Saueressig; O. Schäfer; S.A.R. Kannengiesser

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0740-3194)

Published

2006

Language

EN

Field

Medicine (Health Sciences)