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Cited by 477

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Fat quantification with IDEAL gradient echo imaging: Correction of bias from T1 and noise

Open access
Liu, Chia‐Ying, McKenzie, Charles A., Yu, Huanzhou, Brittain, Jean H., Reeder, Scott B.·2007·Magnetic Resonance in Medicine
DOI 10.1002/mrm.21301
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Abstract

Quantification of hepatic steatosis is a significant unmet need for the diagnosis and treatment of patients with nonalcoholic fatty liver disease (NAFLD). MRI is capable of separating water and fat signals in order to quantify fatty infiltration of the liver (hepatic steatosis). Unfortunately, fat signal has confounding T1 effects and the nonzero mean noise in low signal‐to‐noise ratio (SNR) magnitude images can lead to incorrect estimation of the true lipid percentage. In this study, the effects of bias from T1 effects and image noise were investigated. An oil/water phantom with volume fat‐fractions ranging linearly from 0% to 100% was designed and validated using a spoiled gradient echo (SPGR) sequence in combination with a chemical‐shift based fat‐water separation method known as iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL). We demonstrated two approaches to reduce the effects of T1: small flip angle (flip angle) and dual flip angle methods. Both methods were shown to effectively minimize deviation of the measured fat‐fraction from its true value. We also demonstrated two methods to reduce noise bias: magnitude discrimination and phase‐constrained reconstruction. Both methods were shown to reduce this noise bias effectively from 15% to less than 1%. Magn Reson Med 58:354–364, 2007. © 2007 Wiley‐Liss, Inc.

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Metadata

Authors
Liu, C., McKenzie, C. A., Yu, H., Brittain, J. H., & Reeder, S. B.
Venue
Magnetic Resonance in Medicine
Publisher
Wiley
Published
2007-07-24
Volume
58
Issue
2
Pages
354-364
ISSN
  • 0740-3194 (ISSN-L)
  • 1522-2594
DOI
10.1002/mrm.21301
OpenAlex
W2069846825
Cited by
477
References
33
FWCI
10.94
Type
journal article
Language
en
Open access
bronze

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