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Magnetic Resonance in Medicine
Article . 2014 . Peer-reviewed
License: Wiley Online Library User Agreement
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Lipid elimination with an echo‐shifting N/2‐ghost acquisition (LEENA) MRI

Authors: Lan, Lu; Shannon B, Donnola; Michaela, Koontz; Mark A, Griswold; Jeffrey L, Duerk; Chris A, Flask;

Lipid elimination with an echo‐shifting N/2‐ghost acquisition (LEENA) MRI

Abstract

PurposeThe Dixon techniques provide uniform water‐fat separation but require multiple image sets, which extend the overall acquisition time. Here, an alternative rapid single acquisition method, lipid elimination with an echo‐shifting N/2‐ghost acquisition (LEENA), was introduced.MethodsThe LEENA method utilized a fast imaging with steady‐state free precession sequence to obtain a single k‐space dataset in which successive k‐space lines are acquired to allow the fat magnetization to precess 180°. The LEENA data were then unghosted using either image‐domain (LEENA‐S) or k‐space domain (LEENA‐G) parallel imaging techniques to reconstruct water‐only and fat‐only images. An off‐resonance correction technique was incorporated to improve the uniformity of the water‐fat separation.ResultsUniform water‐fat separation was achieved for both the LEENA‐S and LEENA‐G methods for phantom and human body and leg imaging applications at 1.5T and 3T. The resultant water and fat images were qualitatively similar to conventional 2‐point Dixon and fat‐suppressed images.ConclusionThe LEENA‐S and LEENA‐G methods provide uniform water and fat images from a single MRI acquisition. These straightforward methods can be adapted to 1.5T and 3T clinical MRI scanners and provide comparable fat/water separation with conventional 2‐point Dixon and fat‐suppression techniques. Magn Reson Med 73:711–717, 2015. © 2014 Wiley Periodicals, Inc.

Related Organizations
Keywords

Reproducibility of Results, Image Enhancement, Lipids, Magnetic Resonance Imaging, Sensitivity and Specificity, Pattern Recognition, Automated, Adipose Tissue, Subtraction Technique, Image Interpretation, Computer-Assisted, Humans, Artifacts, Algorithms

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
bronze