Sensitivity of chemical shift-encoded fat quantification to calibration of fat MR spectrum

Purpose To evaluate the impact of different fat spectral models on proton density fat fraction quantification using chemical shift‐encoded MRI (CSE‐MRI). Methods In a simulation study, spectral models of fat were compared pairwise. Comparison of magnitude fitting and mixed fitting was performed over...

Full description

Saved in:
Bibliographic Details
Published inMagnetic resonance in medicine Vol. 75; no. 2; pp. 845 - 851
Main Authors Wang, Xiaoke, Hernando, Diego, Reeder, Scott B.
Format Journal Article
LanguageEnglish
Published United States Blackwell Publishing Ltd 01.02.2016
Wiley Subscription Services, Inc
Subjects
Online AccessGet full text

Cover

Loading…
Abstract Purpose To evaluate the impact of different fat spectral models on proton density fat fraction quantification using chemical shift‐encoded MRI (CSE‐MRI). Methods In a simulation study, spectral models of fat were compared pairwise. Comparison of magnitude fitting and mixed fitting was performed over a range of echo times and fat fractions. In vivo acquisitions from 41 patients were reconstructed using seven published spectral models of fat. T2‐corrected stimulated echo acquisition mode MR spectroscopy was used as a reference. Results The simulations demonstrated that imperfectly calibrated spectral models of fat result in biases that depend on echo times and fat fraction. Mixed fitting was more robust against this bias than magnitude fitting. Multipeak spectral models showed much smaller differences among themselves than from the single‐peak spectral model. In vivo studies showed that all multipeak models agreed better (for mixed fitting, the slope ranged from 0.967 to 1.045 using linear regression) with the reference standard than the single‐peak model (for mixed fitting, slope = 0.76). Conclusion It is essential to use a multipeak fat model for accurate quantification of fat with CSE‐MRI. Furthermore, fat quantification techniques using multipeak fat models are comparable, and no specific choice of spectral model has been shown to be superior to the rest. Magn Reson Med 75:845–851, 2016. © 2015 Wiley Periodicals, Inc.
AbstractList Purpose To evaluate the impact of different fat spectral models on proton density fat fraction quantification using chemical shift-encoded MRI (CSE-MRI). Methods In a simulation study, spectral models of fat were compared pairwise. Comparison of magnitude fitting and mixed fitting was performed over a range of echo times and fat fractions. In vivo acquisitions from 41 patients were reconstructed using seven published spectral models of fat. T2-corrected stimulated echo acquisition mode MR spectroscopy was used as a reference. Results The simulations demonstrated that imperfectly calibrated spectral models of fat result in biases that depend on echo times and fat fraction. Mixed fitting was more robust against this bias than magnitude fitting. Multipeak spectral models showed much smaller differences among themselves than from the single-peak spectral model. In vivo studies showed that all multipeak models agreed better (for mixed fitting, the slope ranged from 0.967 to 1.045 using linear regression) with the reference standard than the single-peak model (for mixed fitting, slope=0.76). Conclusion It is essential to use a multipeak fat model for accurate quantification of fat with CSE-MRI. Furthermore, fat quantification techniques using multipeak fat models are comparable, and no specific choice of spectral model has been shown to be superior to the rest. Magn Reson Med 75:845-851, 2016.
To evaluate the impact of different fat spectral models on proton density fat fraction quantification using chemical shift-encoded MRI (CSE-MRI). In a simulation study, spectral models of fat were compared pairwise. Comparison of magnitude fitting and mixed fitting was performed over a range of echo times and fat fractions. In vivo acquisitions from 41 patients were reconstructed using seven published spectral models of fat. T2-corrected stimulated echo acquisition mode MR spectroscopy was used as a reference. The simulations demonstrated that imperfectly calibrated spectral models of fat result in biases that depend on echo times and fat fraction. Mixed fitting was more robust against this bias than magnitude fitting. Multipeak spectral models showed much smaller differences among themselves than from the single-peak spectral model. In vivo studies showed that all multipeak models agreed better (for mixed fitting, the slope ranged from 0.967 to 1.045 using linear regression) with the reference standard than the single-peak model (for mixed fitting, slope = 0.76). It is essential to use a multipeak fat model for accurate quantification of fat with CSE-MRI. Furthermore, fat quantification techniques using multipeak fat models are comparable, and no specific choice of spectral model has been shown to be superior to the rest.
PURPOSETo evaluate the impact of different fat spectral models on proton density fat fraction quantification using chemical shift-encoded MRI (CSE-MRI).METHODSIn a simulation study, spectral models of fat were compared pairwise. Comparison of magnitude fitting and mixed fitting was performed over a range of echo times and fat fractions. In vivo acquisitions from 41 patients were reconstructed using seven published spectral models of fat. T2-corrected stimulated echo acquisition mode MR spectroscopy was used as a reference.RESULTSThe simulations demonstrated that imperfectly calibrated spectral models of fat result in biases that depend on echo times and fat fraction. Mixed fitting was more robust against this bias than magnitude fitting. Multipeak spectral models showed much smaller differences among themselves than from the single-peak spectral model. In vivo studies showed that all multipeak models agreed better (for mixed fitting, the slope ranged from 0.967 to 1.045 using linear regression) with the reference standard than the single-peak model (for mixed fitting, slope = 0.76).CONCLUSIONIt is essential to use a multipeak fat model for accurate quantification of fat with CSE-MRI. Furthermore, fat quantification techniques using multipeak fat models are comparable, and no specific choice of spectral model has been shown to be superior to the rest.
Purpose To evaluate the impact of different fat spectral models on proton density fat fraction quantification using chemical shift‐encoded MRI (CSE‐MRI). Methods In a simulation study, spectral models of fat were compared pairwise. Comparison of magnitude fitting and mixed fitting was performed over a range of echo times and fat fractions. In vivo acquisitions from 41 patients were reconstructed using seven published spectral models of fat. T2‐corrected stimulated echo acquisition mode MR spectroscopy was used as a reference. Results The simulations demonstrated that imperfectly calibrated spectral models of fat result in biases that depend on echo times and fat fraction. Mixed fitting was more robust against this bias than magnitude fitting. Multipeak spectral models showed much smaller differences among themselves than from the single‐peak spectral model. In vivo studies showed that all multipeak models agreed better (for mixed fitting, the slope ranged from 0.967 to 1.045 using linear regression) with the reference standard than the single‐peak model (for mixed fitting, slope = 0.76). Conclusion It is essential to use a multipeak fat model for accurate quantification of fat with CSE‐MRI. Furthermore, fat quantification techniques using multipeak fat models are comparable, and no specific choice of spectral model has been shown to be superior to the rest. Magn Reson Med 75:845–851, 2016. © 2015 Wiley Periodicals, Inc.
PurposeTo evaluate the impact of different fat spectral models on proton density fat fraction quantification using chemical shift‐encoded MRI (CSE‐MRI).MethodsIn a simulation study, spectral models of fat were compared pairwise. Comparison of magnitude fitting and mixed fitting was performed over a range of echo times and fat fractions. In vivo acquisitions from 41 patients were reconstructed using seven published spectral models of fat. T2‐corrected stimulated echo acquisition mode MR spectroscopy was used as a reference.ResultsThe simulations demonstrated that imperfectly calibrated spectral models of fat result in biases that depend on echo times and fat fraction. Mixed fitting was more robust against this bias than magnitude fitting. Multipeak spectral models showed much smaller differences among themselves than from the single‐peak spectral model. In vivo studies showed that all multipeak models agreed better (for mixed fitting, the slope ranged from 0.967 to 1.045 using linear regression) with the reference standard than the single‐peak model (for mixed fitting, slope = 0.76).ConclusionIt is essential to use a multipeak fat model for accurate quantification of fat with CSE‐MRI. Furthermore, fat quantification techniques using multipeak fat models are comparable, and no specific choice of spectral model has been shown to be superior to the rest. Magn Reson Med 75:845–851, 2016. © 2015 Wiley Periodicals, Inc.
Purpose To evaluate the impact of different fat spectral models on proton density fat fraction quantification using chemical shift-encoded MRI (CSE-MRI). Methods In a simulation study, spectral models of fat were compared pairwise. Comparison of magnitude fitting and mixed fitting was performed over a range of echo times and fat fractions. In vivo acquisitions from 41 patients were reconstructed using seven published spectral models of fat. T2-corrected stimulated echo acquisition mode MR spectroscopy was used as a reference. Results The simulations demonstrated that imperfectly calibrated spectral models of fat result in biases that depend on echo times and fat fraction. Mixed fitting was more robust against this bias than magnitude fitting. Multipeak spectral models showed much smaller differences among themselves than from the single-peak spectral model. In vivo studies showed that all multipeak models agreed better (for mixed fitting, the slope ranged from 0.967 to 1.045 using linear regression) with the reference standard than the single-peak model (for mixed fitting, slope=0.76). Conclusion It is essential to use a multipeak fat model for accurate quantification of fat with CSE-MRI. Furthermore, fat quantification techniques using multipeak fat models are comparable, and no specific choice of spectral model has been shown to be superior to the rest. Magn Reson Med 75:845-851, 2016. © 2015 Wiley Periodicals, Inc.
Author Reeder, Scott B.
Wang, Xiaoke
Hernando, Diego
AuthorAffiliation 4 Medicine, University of Wisconsin, Madison, WI
3 Medical Physics, University of Wisconsin, Madison, WI
2 Biomedical Engineering, University of Wisconsin, Madison, WI
5 Emergency Medicine, University of Wisconsin, Madison, WI
1 Departments of Radiology, University of Wisconsin, Madison, WI
AuthorAffiliation_xml – name: 2 Biomedical Engineering, University of Wisconsin, Madison, WI
– name: 4 Medicine, University of Wisconsin, Madison, WI
– name: 1 Departments of Radiology, University of Wisconsin, Madison, WI
– name: 3 Medical Physics, University of Wisconsin, Madison, WI
– name: 5 Emergency Medicine, University of Wisconsin, Madison, WI
Author_xml – sequence: 1
  givenname: Xiaoke
  surname: Wang
  fullname: Wang, Xiaoke
  organization: Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
– sequence: 2
  givenname: Diego
  surname: Hernando
  fullname: Hernando, Diego
  organization: Department of Radiology, University of Wisconsin, Wisconsin, Madison, USA
– sequence: 3
  givenname: Scott B.
  surname: Reeder
  fullname: Reeder, Scott B.
  email: sreeder@wisc.edu
  organization: Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
BackLink https://www.ncbi.nlm.nih.gov/pubmed/25845713$$D View this record in MEDLINE/PubMed
BookMark eNqNkl1rFDEYhYNU7LZ64R-QAW_0Ytp8f9wIumhVdhWqIngTsjOJmzoz2SaZtvvvTbu7RYuKVyG8zznh5D0HYG8IgwXgMYJHCEJ83Mf-CDMu0T0wQQzjGjNF98AECgprghTdBwcpnUEIlRL0AdjHTFImEJmAb5_skHz2Fz6vq-CqZml735iuSkvvcm2HJrS2rZzJ1flohuxdmWYfhiqHqnB-ETfXor2G5qdVWtkmx7F_CO470yX7aHsegi9vXn-evq1nH0_eTV_O6oZDiWrnuKJOtW4BWwUlVpA4jlvouCQEGscJMVBasSBG0hYJQSylWHDbMkMbZcgheLHxXY2L3raNHXI0nV5F35u41sF4_ftk8Ev9PVxoyhQWkhWDZ1uDGM5Hm7LufWps15nBhjHp8ibnTGJB_gMtmSRBTBT06R30LIxxKD-hMYMKIyn5PykkmGCKEwwL9eTXiLfZdnsswPEGaGJIKVqnG59vFlMS-04jqK-boktT9E1TiuL5HcXO9E_s1v3Sd3b9d1DPT-c7Rb1R-JTt1a3CxB-6hBZMf_1wovlsSvlUvdLvyU9AVNtE
CODEN MRMEEN
CitedBy_id crossref_primary_10_1002_nbm_4691
crossref_primary_10_1002_mrm_28221
crossref_primary_10_1002_mrm_26485
crossref_primary_10_1002_mrm_29076
crossref_primary_10_1016_j_ejrad_2024_111709
crossref_primary_10_1016_j_ejrad_2017_05_028
crossref_primary_10_1002_mrm_27173
crossref_primary_10_1007_s00330_017_5141_0
crossref_primary_10_1097_RLI_0000000000001092
crossref_primary_10_1055_s_0044_1788693
crossref_primary_10_1002_mrm_28785
crossref_primary_10_1016_j_clinimag_2018_01_011
crossref_primary_10_1016_j_mri_2016_11_011
crossref_primary_10_1002_jmri_26661
crossref_primary_10_1371_journal_pone_0206735
crossref_primary_10_1016_j_bonr_2020_100259
crossref_primary_10_1016_j_mri_2022_04_004
crossref_primary_10_3389_fnut_2019_00005
crossref_primary_10_1002_mrm_29860
crossref_primary_10_1002_jmri_25769
crossref_primary_10_1016_j_heliyon_2024_e28468
crossref_primary_10_1002_jmri_25845
crossref_primary_10_1002_jmri_26219
crossref_primary_10_1007_s10334_020_00901_0
crossref_primary_10_1016_j_mri_2025_110340
crossref_primary_10_1002_nbm_5217
crossref_primary_10_1002_mus_27399
crossref_primary_10_1002_jmri_27260
crossref_primary_10_3390_cells8080845
crossref_primary_10_1007_s10334_024_01148_9
crossref_primary_10_1186_s12880_016_0167_3
crossref_primary_10_1002_mrm_29697
crossref_primary_10_1016_j_crad_2020_07_031
crossref_primary_10_1016_j_compbiomed_2024_108448
crossref_primary_10_1097_RMR_0000000000000141
crossref_primary_10_4236_ojo_2022_123010
crossref_primary_10_1016_j_mri_2016_08_006
crossref_primary_10_1016_j_mri_2018_06_012
crossref_primary_10_1007_s00330_020_07010_5
crossref_primary_10_1002_mrm_29084
crossref_primary_10_3389_fendo_2020_00421
crossref_primary_10_3348_kjr_2019_0002
crossref_primary_10_1002_mrm_26693
crossref_primary_10_1007_s11914_020_00562_x
crossref_primary_10_1016_j_mri_2018_07_001
crossref_primary_10_1002_mrm_28919
crossref_primary_10_1007_s00261_017_1048_0
crossref_primary_10_1002_jmri_25382
crossref_primary_10_1002_nbm_4906
crossref_primary_10_1002_mrm_30390
Cites_doi 10.1002/mrm.23044
10.1002/mrm.22177
10.1002/jmri.21682
10.1002/mrm.21481
10.1002/mrm.22840
10.1194/jlr.D800010-JLR200
10.1148/radiol.2511080666
10.1016/j.mri.2007.08.012
10.1002/mrm.23185
10.1152/ajpendo.00064.2004
10.1148/radiol.10100659
10.1002/mrm.20624
10.1007/BF02668096
10.1148/radiol.12112520
10.1002/jmri.21751
10.1002/jmri.20831
10.1002/mrm.25054
10.1148/radiol.2303021331
10.1002/nbm.1622
10.1002/mrm.23016
10.1002/jmri.23741
10.1002/mrm.21737
10.1148/radiol.10100708
10.1016/j.mri.2008.01.050
10.1006/jmre.1997.1244
10.1381/0960892053576596
10.1002/jmri.23835
10.1002/jmri.23998
10.1016/0730-725X(94)92543-7
10.1002/mrm.21301
10.1097/RLI.0b013e3181862413
10.1002/mrm.24951
10.1016/j.mric.2010.08.013
10.1002/jmri.22514
10.1148/rg.291075123
10.1002/mrm.22455
10.1038/oby.2009.352
ContentType Journal Article
Copyright 2015 Wiley Periodicals, Inc.
2016 Wiley Periodicals, Inc.
Copyright_xml – notice: 2015 Wiley Periodicals, Inc.
– notice: 2016 Wiley Periodicals, Inc.
DBID BSCLL
AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
8FD
FR3
K9.
M7Z
P64
7X8
7QO
5PM
DOI 10.1002/mrm.25681
DatabaseName Istex
CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
Technology Research Database
Engineering Research Database
ProQuest Health & Medical Complete (Alumni)
Biochemistry Abstracts 1
Biotechnology and BioEngineering Abstracts
MEDLINE - Academic
Biotechnology Research Abstracts
PubMed Central (Full Participant titles)
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
Biochemistry Abstracts 1
ProQuest Health & Medical Complete (Alumni)
Engineering Research Database
Technology Research Database
Biotechnology and BioEngineering Abstracts
MEDLINE - Academic
Biotechnology Research Abstracts
DatabaseTitleList Engineering Research Database
MEDLINE
MEDLINE - Academic

Biochemistry Abstracts 1
Biochemistry Abstracts 1
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 2
  dbid: EIF
  name: MEDLINE
  url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search
  sourceTypes: Index Database
DeliveryMethod fulltext_linktorsrc
Discipline Medicine
Physics
EISSN 1522-2594
EndPage 851
ExternalDocumentID PMC4592785
3923832081
25845713
10_1002_mrm_25681
MRM25681
ark_67375_WNG_6LC46C9B_J
Genre shortCommunication
Research Support, Non-U.S. Gov't
Journal Article
Research Support, N.I.H., Extramural
GrantInformation_xml – fundername: GE Healthcare
– fundername: National Institutes of Health
  funderid: R01‐DK083380; R01‐DK088925; K24‐DK102595; UL1TR00427
– fundername: NIDDK NIH HHS
  grantid: R01- DK088925
– fundername: NIDDK NIH HHS
  grantid: R01 DK083380
– fundername: NIDDK NIH HHS
  grantid: R01 DK088925
– fundername: NCATS NIH HHS
  grantid: UL1 TR000427
– fundername: NIDDK NIH HHS
  grantid: K24 DK102595
– fundername: NIDDK NIH HHS
  grantid: K24-DK102595
– fundername: NIDDK NIH HHS
  grantid: R01 DK100651
– fundername: NCATS NIH HHS
  grantid: UL1TR00427
– fundername: NIDDK NIH HHS
  grantid: R01-DK083380
GroupedDBID ---
-DZ
.3N
.55
.GA
.Y3
05W
0R~
10A
1L6
1OB
1OC
1ZS
24P
31~
33P
3O-
3SF
3WU
4.4
4ZD
50Y
50Z
51W
51X
52M
52N
52O
52P
52R
52S
52T
52U
52V
52W
52X
53G
5GY
5RE
5VS
66C
702
7PT
8-0
8-1
8-3
8-4
8-5
8UM
930
A01
A03
AAESR
AAEVG
AAHHS
AANLZ
AAONW
AASGY
AAXRX
AAZKR
ABCQN
ABCUV
ABDPE
ABEML
ABIJN
ABJNI
ABLJU
ABPVW
ABQWH
ABXGK
ACAHQ
ACBWZ
ACCFJ
ACCZN
ACFBH
ACGFO
ACGFS
ACGOF
ACIWK
ACMXC
ACPOU
ACPRK
ACSCC
ACXBN
ACXQS
ADBBV
ADBTR
ADEOM
ADIZJ
ADKYN
ADMGS
ADOZA
ADXAS
ADZMN
AEEZP
AEGXH
AEIGN
AEIMD
AENEX
AEQDE
AEUQT
AEUYR
AFBPY
AFFNX
AFFPM
AFGKR
AFPWT
AFRAH
AFZJQ
AHBTC
AHMBA
AIACR
AIAGR
AITYG
AIURR
AIWBW
AJBDE
ALAGY
ALMA_UNASSIGNED_HOLDINGS
ALUQN
AMBMR
AMYDB
ASPBG
ATUGU
AVWKF
AZBYB
AZFZN
AZVAB
BAFTC
BDRZF
BFHJK
BHBCM
BMXJE
BROTX
BRXPI
BSCLL
BY8
C45
CS3
D-6
D-7
D-E
D-F
DCZOG
DPXWK
DR2
DRFUL
DRMAN
DRSTM
DU5
EBD
EBS
EJD
EMOBN
F00
F01
F04
FEDTE
FUBAC
G-S
G.N
GNP
GODZA
H.X
HBH
HDBZQ
HF~
HGLYW
HHY
HHZ
HVGLF
HZ~
I-F
IX1
J0M
JPC
KBYEO
KQQ
LATKE
LAW
LC2
LC3
LEEKS
LH4
LITHE
LOXES
LP6
LP7
LUTES
LW6
LYRES
M65
MEWTI
MK4
MRFUL
MRMAN
MRSTM
MSFUL
MSMAN
MSSTM
MXFUL
MXMAN
MXSTM
N04
N05
N9A
NF~
NNB
O66
O9-
OIG
OVD
P2P
P2W
P2X
P2Z
P4B
P4D
PALCI
PQQKQ
Q.N
Q11
QB0
QRW
R.K
RGB
RIWAO
RJQFR
ROL
RWI
RX1
RYL
SAMSI
SUPJJ
SV3
TEORI
TUS
TWZ
UB1
V2E
V8K
W8V
W99
WBKPD
WHWMO
WIB
WIH
WIJ
WIK
WIN
WJL
WOHZO
WQJ
WRC
WUP
WVDHM
WXI
WXSBR
X7M
XG1
XPP
XV2
ZGI
ZXP
ZZTAW
~IA
~WT
AAHQN
AAIPD
AAMNL
AANHP
AAYCA
ACRPL
ACYXJ
ADNMO
AFWVQ
ALVPJ
AAYXX
AEYWJ
AGHNM
AGQPQ
AGYGG
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
8FD
AAMMB
AEFGJ
AGXDD
AIDQK
AIDYY
FR3
K9.
M7Z
P64
7X8
7QO
5PM
ID FETCH-LOGICAL-c6081-ff694f9dfb0d9082903f62d0f68330af633a08e7b3a84d1773e44276ed5a4c9a3
IEDL.DBID DR2
ISSN 0740-3194
IngestDate Thu Aug 21 18:40:04 EDT 2025
Fri Jul 11 06:29:42 EDT 2025
Fri Jul 11 10:27:07 EDT 2025
Fri Jul 25 12:05:58 EDT 2025
Fri Jul 25 12:11:00 EDT 2025
Thu Apr 03 07:01:09 EDT 2025
Thu Apr 24 22:58:35 EDT 2025
Tue Jul 01 01:20:58 EDT 2025
Wed Jan 22 17:05:47 EST 2025
Wed Oct 30 10:05:32 EDT 2024
IsDoiOpenAccess false
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 2
Keywords fat spectrum
fat quantification
magnetic resonance imaging
nonalcoholic fatty liver disease
proton density fat fraction
spectral model of fat
Language English
License 2015 Wiley Periodicals, Inc.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c6081-ff694f9dfb0d9082903f62d0f68330af633a08e7b3a84d1773e44276ed5a4c9a3
Notes National Institutes of Health - No. R01-DK083380; No. R01-DK088925; No. K24-DK102595; No. UL1TR00427
GE Healthcare
ark:/67375/WNG-6LC46C9B-J
istex:0FA518F64B5A493BB201C4899B5E6AA0F9472334
ArticleID:MRM25681
Correction added after online publication 29 April 2015. The caption for figure 2 has been updated to correctly reference the figure's subparts: “are presented for mixed fitting (a‐e) and for magnitude fitting (f‐j)” on line 3 has been changed to “are presented for mixed fitting (f‐j) and for magnitude fitting (a‐e).”
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
OpenAccessLink http://doi.org/10.1002/mrm.25681
PMID 25845713
PQID 1757596320
PQPubID 1016391
PageCount 7
ParticipantIDs pubmedcentral_primary_oai_pubmedcentral_nih_gov_4592785
proquest_miscellaneous_1776658273
proquest_miscellaneous_1760883157
proquest_journals_2509218867
proquest_journals_1757596320
pubmed_primary_25845713
crossref_citationtrail_10_1002_mrm_25681
crossref_primary_10_1002_mrm_25681
wiley_primary_10_1002_mrm_25681_MRM25681
istex_primary_ark_67375_WNG_6LC46C9B_J
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate February 2016
PublicationDateYYYYMMDD 2016-02-01
PublicationDate_xml – month: 02
  year: 2016
  text: February 2016
PublicationDecade 2010
PublicationPlace United States
PublicationPlace_xml – name: United States
– name: Hoboken
PublicationTitle Magnetic resonance in medicine
PublicationTitleAlternate Magn. Reson. Med
PublicationYear 2016
Publisher Blackwell Publishing Ltd
Wiley Subscription Services, Inc
Publisher_xml – name: Blackwell Publishing Ltd
– name: Wiley Subscription Services, Inc
References Cassidy FH, Yokoo T, Aganovic L, Hanna RF, Bydder M, Middleton MS, Hamilton G, Chavez AD, Schwimmer JB, Sirlin CB. Fatty liver disease: MR imaging techniques for the detection and quantification of liver steatosis. Radiographics 2009;29:231-260.
Schwenzer NF, Machann J, Haap MM, et al. T2* relaxometry in liver, pancreas, and spleen in a healthy cohort of one hundred twenty-nine subjects-correlation with age, gender, and serum ferritin. Invest Radiol 2008;43:854-860.
de Bazelaire CM, Duhamel GD, Rofsky NM, Alsop DC. MR imaging relaxation times of abdominal and pelvic tissues measured in vivo at 3.0 T: preliminary results. Radiology 2004;230:652-659.
Brau AC, Beatty PJ, Skare S, Bammer R. Comparison of reconstruction accuracy and efficiency among autocalibrating data-driven parallel imaging methods. Magn Reson Med 2008;59:382-395.
Bydder M, Hamilton G, Yokoo T, Sirlin CB. Optimal phased-array combination for spectroscopy. Magn Reson Imaging 2008;26:847-850.
Thomsen C, Becker U, Winkler K, Christoffersen P, Jensen M, Henriksen O. Quantification of liver fat using magnetic resonance spectroscopy. Magn Reson Imaging 1994;12:487-495.
Hernando D, Hines CD, Yu H, Reeder SB. Addressing phase errors in fat-water imaging using a mixed magnitude/complex fitting method. Magn Reson Med 2012;67:638-644.
Yu H, Shimakawa A, Hines CD, McKenzie CA, Hamilton G, Sirlin CB, Brittain JH, Reeder SB. Combination of complex-based and magnitude-based multiecho water-fat separation for accurate quantification of fat-fraction. Magn Reson Med 2011;66:199-206.
Hernando D, Sharma SD, Kramer H, Reeder SB. On the confounding effect of temperature on chemical shift-encoded fat quantification. Magn Reson Med 2014;72:464-470.
Hernando D, Liang ZP, Kellman P. Chemical shift-based water/fat separation: a comparison of signal models. Magn Reson Med 2010;64:811-822.
Bydder M, Yokoo T, Hamilton G, Middleton MS, Chavez AD, Schwimmer JB, Lavine JE, Sirlin CB. Relaxation effects in the quantification of fat using gradient echo imaging. Magn Reson Imaging 2008;26:347-359.
Reeder SB, Robson PM, Yu H, Shimakawa A, Hines CD, McKenzie CA, Brittain JH. Quantification of hepatic steatosis with MRI: the effects of accurate fat spectral modeling. J Magn Reson Imaging 2009;29:1332-1339.
Hines CD, Frydrychowicz A, Hamilton G, Tudorascu DL, Vigen KK, Yu H, McKenzie CA, Sirlin CB, Brittain JH, Reeder SB. T(1) independent, T(2) (*) corrected chemical shift based fat-water separation with multi-peak fat spectral modeling is an accurate and precise measure of hepatic steatosis. J Magn Reson Imaging 2011;33:873-881.
Zhong X, Nickel MD, Kannengiesser SA, Dale BM, Kiefer B, Bashir MR. Liver fat quantification using a multi-step adaptive fitting approach with multi-echo GRE imaging. Magn Reson Med 2014;72:1353-1365.
Yokoo T, Shiehmorteza M, Hamilton G, et al. Estimation of hepatic proton-density fat fraction by using MR imaging at 3.0 T. Radiology 2011;258:749-759.
Yu H, Shimakawa A, McKenzie CA, Brodsky E, Brittain JH, Reeder SB. Multiecho water-fat separation and simultaneous R2* estimation with multifrequency fat spectrum modeling. Magn Reson Med 2008;60:1122-1134.
Ren J, Dimitrov I, Sherry AD, Malloy CR. Composition of adipose tissue and marrow fat in humans by 1H NMR at 7 Tesla. J Lipid Res 2008;49:2055-2062.
Reeder SB, Bice EK, Yu H, Hernando D, Pineda AR. On the performance of T2* correction methods for quantification of hepatic fat content. Magn Reson Med 2012;67:389-404.
Sharma P, Martin DR, Pineda N, Xu Q, Vos M, Anania F, Hu X. Quantitative analysis of T2-correction in single-voxel magnetic resonance spectroscopy of hepatic lipid fraction. J Magn Reson Imaging 2009;29:629-635.
Hamilton G, Yokoo T, Bydder M, Cruite I, Schroeder ME, Sirlin CB, Middleton MS. In vivo characterization of the liver fat (1)H MR spectrum. NMR Biomed 2011;24:784-790.
Hernando D, Kellman P, Haldar JP, Liang ZP. Robust water/fat separation in the presence of large field inhomogeneities using a graph cut algorithm. Magn Reson Med 2010;63:79-90.
Wokke BH, Bos C, Reijnierse M, van Rijswijk CS, Eggers H, Webb A, Verschuuren JJ, Kan HE. Comparison of dixon and T1-weighted MR methods to assess the degree of fat infiltration in duchenne muscular dystrophy patients. J Magn Reson Imaging 2013;38:619-624.
Reeder SB, Hu HH, Sirlin CB. Proton density fat-fraction: a standardized MR-based biomarker of tissue fat concentration. J Magn Reson Imaging 2012;36:1011-1014.
Yokoo T, Bydder M, Hamilton G, et al. Nonalcoholic fatty liver disease: diagnostic and fat-grading accuracy of low-flip-angle multiecho gradient-recalled-echo MR imaging at 1.5 T. Radiology 2009;251:67-76.
Cuadrado A, Orive A, Garcia-Suarez C, Dominguez A, Fernandez-Escalante JC, Crespo J, Pons-Romero F. Non-alcoholic steatohepatitis (NASH) and hepatocellular carcinoma. Obes Surg 2005;15:442-446.
Reeder SB, Pineda AR, Wen Z, Shimakawa A, Yu H, Brittain JH, Gold GE, Beaulieu CH, Pelc NJ. Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL): application with fast spin-echo imaging. Magn Reson Med 2005;54:636-644.
Reeder SB, McKenzie CA, Pineda AR, Yu H, Shimakawa A, Brau AC, Hargreaves BA, Gold GE, Brittain JH. Water-fat separation with IDEAL gradient-echo imaging. J Magn Reson Imaging 2007;25:644-652.
Reeder SB, Sirlin CB. Quantification of liver fat with magnetic resonance imaging. Magn Reson Imaging Clin N Am 2010;18:337-357, ix.
Kuhn JP, Hernando D, Munoz del Rio A, Evert M, Kannengiesser S, Volzke H, Mensel B, Puls R, Hosten N, Reeder SB. Effect of multipeak spectral modeling of fat for liver iron and fat quantification: correlation of biopsy with MR imaging results. Radiology 2012;265:133-142.
Liu CY, McKenzie CA, Yu H, Brittain JH, Reeder SB. Fat quantification with IDEAL gradient echo imaging: correction of bias from T(1) and noise. Magn Reson Med 2007;58:354-364.
Vanhamme L, van den Boogaart A, Van Huffel S. Improved method for accurate and efficient quantification of MRS data with use of prior knowledge. J Magn Reson 1997;129:35-43.
Meisamy S, Hines CD, Hamilton G, Sirlin CB, McKenzie CA, Yu H, Brittain JH, Reeder SB. Quantification of hepatic steatosis with T1-independent, T2-corrected MR imaging with spectral modeling of fat: blinded comparison with MR spectroscopy. Radiology 2011;258:767-775.
Szczepaniak LS, Nurenberg P, Leonard D, Browning JD, Reingold JS, Grundy S, Hobbs HH, Dobbins RL. Magnetic resonance spectroscopy to measure hepatic triglyceride content: prevalence of hepatic steatosis in the general population. Am J Physiol Endocrinol Metab 2005;288:E462-E468.
Smedile A, Bugianesi E. Steatosis and hepatocellular carcinoma risk. Eur Rev Med Pharmacol Sci 2005;9:291-293.
Horng DE, Hernando D, Hines CD, Reeder SB. Comparison of R2* correction methods for accurate fat quantification in fatty liver. J Magn Reson Imaging 2013;37:414-422.
Berglund J, Kullberg J. Three-dimensional water/fat separation and T2* estimation based on whole-image optimization-application in breathhold liver imaging at 1.5 T. Magn Reson Med 2012;67:1684-1693.
Hu HH, Kim HW, Nayak KS, Goran MI. Comparison of fat-water MRI and single-voxel MRS in the assessment of hepatic and pancreatic fat fractions in humans. Obesity (Silver Spring) 2010;18:841-847.
Naressi A, Couturier C, Devos JM, Janssen M, Mangeat C, de Beer R, Graveron-Demilly D. Java-based graphical user interface for the MRUI quantitation package. MAGMA 2001;12:141-152.
2011; 258
2012; 265
2010; 18
2008; 59
2011; 33
2009; 251
2012; 36
2010; 63
2007; 58
2009; 29
2010; 64
2004; 230
2013; 37
1997; 129
2013; 38
2005; 288
2005; 9
2008; 49
1994; 12
2008; 26
2011; 66
2005; 54
2011; 24
2008; 43
2005; 15
2001; 12
2014; 72
2012; 67
2008; 60
2007; 25
e_1_2_7_6_1
e_1_2_7_5_1
e_1_2_7_4_1
e_1_2_7_9_1
e_1_2_7_8_1
e_1_2_7_7_1
e_1_2_7_19_1
e_1_2_7_18_1
e_1_2_7_17_1
e_1_2_7_16_1
e_1_2_7_40_1
e_1_2_7_2_1
e_1_2_7_15_1
e_1_2_7_41_1
e_1_2_7_14_1
e_1_2_7_13_1
e_1_2_7_12_1
e_1_2_7_11_1
e_1_2_7_10_1
e_1_2_7_26_1
e_1_2_7_27_1
e_1_2_7_28_1
e_1_2_7_29_1
e_1_2_7_30_1
e_1_2_7_25_1
e_1_2_7_31_1
e_1_2_7_24_1
e_1_2_7_32_1
e_1_2_7_23_1
e_1_2_7_33_1
e_1_2_7_22_1
e_1_2_7_34_1
e_1_2_7_21_1
e_1_2_7_35_1
e_1_2_7_20_1
e_1_2_7_36_1
e_1_2_7_37_1
e_1_2_7_38_1
Smedile A (e_1_2_7_3_1) 2005; 9
e_1_2_7_39_1
14990831 - Radiology. 2004 Mar;230(3):652-9
18093781 - Magn Reson Imaging. 2008 Apr;26(3):347-59
23165934 - J Magn Reson Imaging. 2013 Feb;37(2):414-22
19002057 - Invest Radiol. 2008 Dec;43(12):854-60
9405214 - J Magn Reson. 1997 Nov;129(1):35-43
19168847 - Radiographics. 2009 Jan-Feb;29(1):231-60
16092103 - Magn Reson Med. 2005 Sep;54(3):636-44
11390270 - MAGMA. 2001 May;12(2-3):141-52
19243059 - J Magn Reson Imaging. 2009 Mar;29(3):629-35
24123362 - Magn Reson Med. 2014 Aug;72(2):464-70
23292884 - J Magn Reson Imaging. 2013 Sep;38(3):619-24
21834002 - NMR Biomed. 2011 Aug;24(7):784-90
21212366 - Radiology. 2011 Mar;258(3):749-59
17326087 - J Magn Reson Imaging. 2007 Mar;25(3):644-52
16231592 - Eur Rev Med Pharmacol Sci. 2005 Sep-Oct;9(5):291-3
21248233 - Radiology. 2011 Mar;258(3):767-75
15339742 - Am J Physiol Endocrinol Metab. 2005 Feb;288(2):E462-8
21094444 - Magn Reson Imaging Clin N Am. 2010 Aug;18(3):337-57, ix
21448952 - J Magn Reson Imaging. 2011 Apr;33(4):873-81
20593375 - Magn Reson Med. 2010 Sep;64(3):811-22
18486392 - Magn Reson Imaging. 2008 Jul;26(6):847-50
22189760 - Magn Reson Med. 2012 Jun;67(6):1684-93
19472390 - J Magn Reson Imaging. 2009 Jun;29(6):1332-9
8007779 - Magn Reson Imaging. 1994;12(3):487-95
21661045 - Magn Reson Med. 2012 Feb;67(2):389-404
21695724 - Magn Reson Med. 2011 Jul;66(1):199-206
22923718 - Radiology. 2012 Oct;265(1):133-42
18228603 - Magn Reson Med. 2008 Feb;59(2):382-95
18956464 - Magn Reson Med. 2008 Nov;60(5):1122-34
18509197 - J Lipid Res. 2008 Sep;49(9):2055-62
17654578 - Magn Reson Med. 2007 Aug;58(2):354-64
22777847 - J Magn Reson Imaging. 2012 Nov;36(5):1011-4
19859956 - Magn Reson Med. 2010 Jan;63(1):79-90
19221054 - Radiology. 2009 Apr;251(1):67-76
15826485 - Obes Surg. 2005 Mar;15(3):442-6
21713978 - Magn Reson Med. 2012 Mar;67(3):638-44
24323332 - Magn Reson Med. 2014 Nov;72(5):1353-65
References_xml – reference: Kuhn JP, Hernando D, Munoz del Rio A, Evert M, Kannengiesser S, Volzke H, Mensel B, Puls R, Hosten N, Reeder SB. Effect of multipeak spectral modeling of fat for liver iron and fat quantification: correlation of biopsy with MR imaging results. Radiology 2012;265:133-142.
– reference: Hernando D, Hines CD, Yu H, Reeder SB. Addressing phase errors in fat-water imaging using a mixed magnitude/complex fitting method. Magn Reson Med 2012;67:638-644.
– reference: Berglund J, Kullberg J. Three-dimensional water/fat separation and T2* estimation based on whole-image optimization-application in breathhold liver imaging at 1.5 T. Magn Reson Med 2012;67:1684-1693.
– reference: Hernando D, Sharma SD, Kramer H, Reeder SB. On the confounding effect of temperature on chemical shift-encoded fat quantification. Magn Reson Med 2014;72:464-470.
– reference: Yu H, Shimakawa A, Hines CD, McKenzie CA, Hamilton G, Sirlin CB, Brittain JH, Reeder SB. Combination of complex-based and magnitude-based multiecho water-fat separation for accurate quantification of fat-fraction. Magn Reson Med 2011;66:199-206.
– reference: Bydder M, Hamilton G, Yokoo T, Sirlin CB. Optimal phased-array combination for spectroscopy. Magn Reson Imaging 2008;26:847-850.
– reference: Meisamy S, Hines CD, Hamilton G, Sirlin CB, McKenzie CA, Yu H, Brittain JH, Reeder SB. Quantification of hepatic steatosis with T1-independent, T2-corrected MR imaging with spectral modeling of fat: blinded comparison with MR spectroscopy. Radiology 2011;258:767-775.
– reference: Reeder SB, Bice EK, Yu H, Hernando D, Pineda AR. On the performance of T2* correction methods for quantification of hepatic fat content. Magn Reson Med 2012;67:389-404.
– reference: Hernando D, Kellman P, Haldar JP, Liang ZP. Robust water/fat separation in the presence of large field inhomogeneities using a graph cut algorithm. Magn Reson Med 2010;63:79-90.
– reference: Szczepaniak LS, Nurenberg P, Leonard D, Browning JD, Reingold JS, Grundy S, Hobbs HH, Dobbins RL. Magnetic resonance spectroscopy to measure hepatic triglyceride content: prevalence of hepatic steatosis in the general population. Am J Physiol Endocrinol Metab 2005;288:E462-E468.
– reference: Reeder SB, Robson PM, Yu H, Shimakawa A, Hines CD, McKenzie CA, Brittain JH. Quantification of hepatic steatosis with MRI: the effects of accurate fat spectral modeling. J Magn Reson Imaging 2009;29:1332-1339.
– reference: de Bazelaire CM, Duhamel GD, Rofsky NM, Alsop DC. MR imaging relaxation times of abdominal and pelvic tissues measured in vivo at 3.0 T: preliminary results. Radiology 2004;230:652-659.
– reference: Smedile A, Bugianesi E. Steatosis and hepatocellular carcinoma risk. Eur Rev Med Pharmacol Sci 2005;9:291-293.
– reference: Horng DE, Hernando D, Hines CD, Reeder SB. Comparison of R2* correction methods for accurate fat quantification in fatty liver. J Magn Reson Imaging 2013;37:414-422.
– reference: Cassidy FH, Yokoo T, Aganovic L, Hanna RF, Bydder M, Middleton MS, Hamilton G, Chavez AD, Schwimmer JB, Sirlin CB. Fatty liver disease: MR imaging techniques for the detection and quantification of liver steatosis. Radiographics 2009;29:231-260.
– reference: Reeder SB, McKenzie CA, Pineda AR, Yu H, Shimakawa A, Brau AC, Hargreaves BA, Gold GE, Brittain JH. Water-fat separation with IDEAL gradient-echo imaging. J Magn Reson Imaging 2007;25:644-652.
– reference: Cuadrado A, Orive A, Garcia-Suarez C, Dominguez A, Fernandez-Escalante JC, Crespo J, Pons-Romero F. Non-alcoholic steatohepatitis (NASH) and hepatocellular carcinoma. Obes Surg 2005;15:442-446.
– reference: Hines CD, Frydrychowicz A, Hamilton G, Tudorascu DL, Vigen KK, Yu H, McKenzie CA, Sirlin CB, Brittain JH, Reeder SB. T(1) independent, T(2) (*) corrected chemical shift based fat-water separation with multi-peak fat spectral modeling is an accurate and precise measure of hepatic steatosis. J Magn Reson Imaging 2011;33:873-881.
– reference: Brau AC, Beatty PJ, Skare S, Bammer R. Comparison of reconstruction accuracy and efficiency among autocalibrating data-driven parallel imaging methods. Magn Reson Med 2008;59:382-395.
– reference: Yokoo T, Shiehmorteza M, Hamilton G, et al. Estimation of hepatic proton-density fat fraction by using MR imaging at 3.0 T. Radiology 2011;258:749-759.
– reference: Schwenzer NF, Machann J, Haap MM, et al. T2* relaxometry in liver, pancreas, and spleen in a healthy cohort of one hundred twenty-nine subjects-correlation with age, gender, and serum ferritin. Invest Radiol 2008;43:854-860.
– reference: Yokoo T, Bydder M, Hamilton G, et al. Nonalcoholic fatty liver disease: diagnostic and fat-grading accuracy of low-flip-angle multiecho gradient-recalled-echo MR imaging at 1.5 T. Radiology 2009;251:67-76.
– reference: Zhong X, Nickel MD, Kannengiesser SA, Dale BM, Kiefer B, Bashir MR. Liver fat quantification using a multi-step adaptive fitting approach with multi-echo GRE imaging. Magn Reson Med 2014;72:1353-1365.
– reference: Reeder SB, Pineda AR, Wen Z, Shimakawa A, Yu H, Brittain JH, Gold GE, Beaulieu CH, Pelc NJ. Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL): application with fast spin-echo imaging. Magn Reson Med 2005;54:636-644.
– reference: Yu H, Shimakawa A, McKenzie CA, Brodsky E, Brittain JH, Reeder SB. Multiecho water-fat separation and simultaneous R2* estimation with multifrequency fat spectrum modeling. Magn Reson Med 2008;60:1122-1134.
– reference: Bydder M, Yokoo T, Hamilton G, Middleton MS, Chavez AD, Schwimmer JB, Lavine JE, Sirlin CB. Relaxation effects in the quantification of fat using gradient echo imaging. Magn Reson Imaging 2008;26:347-359.
– reference: Vanhamme L, van den Boogaart A, Van Huffel S. Improved method for accurate and efficient quantification of MRS data with use of prior knowledge. J Magn Reson 1997;129:35-43.
– reference: Liu CY, McKenzie CA, Yu H, Brittain JH, Reeder SB. Fat quantification with IDEAL gradient echo imaging: correction of bias from T(1) and noise. Magn Reson Med 2007;58:354-364.
– reference: Reeder SB, Hu HH, Sirlin CB. Proton density fat-fraction: a standardized MR-based biomarker of tissue fat concentration. J Magn Reson Imaging 2012;36:1011-1014.
– reference: Hamilton G, Yokoo T, Bydder M, Cruite I, Schroeder ME, Sirlin CB, Middleton MS. In vivo characterization of the liver fat (1)H MR spectrum. NMR Biomed 2011;24:784-790.
– reference: Hernando D, Liang ZP, Kellman P. Chemical shift-based water/fat separation: a comparison of signal models. Magn Reson Med 2010;64:811-822.
– reference: Hu HH, Kim HW, Nayak KS, Goran MI. Comparison of fat-water MRI and single-voxel MRS in the assessment of hepatic and pancreatic fat fractions in humans. Obesity (Silver Spring) 2010;18:841-847.
– reference: Ren J, Dimitrov I, Sherry AD, Malloy CR. Composition of adipose tissue and marrow fat in humans by 1H NMR at 7 Tesla. J Lipid Res 2008;49:2055-2062.
– reference: Wokke BH, Bos C, Reijnierse M, van Rijswijk CS, Eggers H, Webb A, Verschuuren JJ, Kan HE. Comparison of dixon and T1-weighted MR methods to assess the degree of fat infiltration in duchenne muscular dystrophy patients. J Magn Reson Imaging 2013;38:619-624.
– reference: Naressi A, Couturier C, Devos JM, Janssen M, Mangeat C, de Beer R, Graveron-Demilly D. Java-based graphical user interface for the MRUI quantitation package. MAGMA 2001;12:141-152.
– reference: Thomsen C, Becker U, Winkler K, Christoffersen P, Jensen M, Henriksen O. Quantification of liver fat using magnetic resonance spectroscopy. Magn Reson Imaging 1994;12:487-495.
– reference: Sharma P, Martin DR, Pineda N, Xu Q, Vos M, Anania F, Hu X. Quantitative analysis of T2-correction in single-voxel magnetic resonance spectroscopy of hepatic lipid fraction. J Magn Reson Imaging 2009;29:629-635.
– reference: Reeder SB, Sirlin CB. Quantification of liver fat with magnetic resonance imaging. Magn Reson Imaging Clin N Am 2010;18:337-357, ix.
– volume: 58
  start-page: 354
  year: 2007
  end-page: 364
  article-title: Fat quantification with IDEAL gradient echo imaging: correction of bias from T(1) and noise
  publication-title: Magn Reson Med
– volume: 15
  start-page: 442
  year: 2005
  end-page: 446
  article-title: Non‐alcoholic steatohepatitis (NASH) and hepatocellular carcinoma
  publication-title: Obes Surg
– volume: 288
  start-page: E462
  year: 2005
  end-page: E468
  article-title: Magnetic resonance spectroscopy to measure hepatic triglyceride content: prevalence of hepatic steatosis in the general population
  publication-title: Am J Physiol Endocrinol Metab
– volume: 9
  start-page: 291
  year: 2005
  end-page: 293
  article-title: Steatosis and hepatocellular carcinoma risk
  publication-title: Eur Rev Med Pharmacol Sci
– volume: 63
  start-page: 79
  year: 2010
  end-page: 90
  article-title: Robust water/fat separation in the presence of large field inhomogeneities using a graph cut algorithm
  publication-title: Magn Reson Med
– volume: 37
  start-page: 414
  year: 2013
  end-page: 422
  article-title: Comparison of R2* correction methods for accurate fat quantification in fatty liver
  publication-title: J Magn Reson Imaging
– volume: 29
  start-page: 629
  year: 2009
  end-page: 635
  article-title: Quantitative analysis of T2‐correction in single‐voxel magnetic resonance spectroscopy of hepatic lipid fraction
  publication-title: J Magn Reson Imaging
– volume: 43
  start-page: 854
  year: 2008
  end-page: 860
  article-title: T2* relaxometry in liver, pancreas, and spleen in a healthy cohort of one hundred twenty‐nine subjects‐correlation with age, gender, and serum ferritin
  publication-title: Invest Radiol
– volume: 230
  start-page: 652
  year: 2004
  end-page: 659
  article-title: MR imaging relaxation times of abdominal and pelvic tissues measured in vivo at 3.0 T: preliminary results
  publication-title: Radiology
– volume: 12
  start-page: 141
  year: 2001
  end-page: 152
  article-title: Java‐based graphical user interface for the MRUI quantitation package
  publication-title: MAGMA
– volume: 29
  start-page: 231
  year: 2009
  end-page: 260
  article-title: Fatty liver disease: MR imaging techniques for the detection and quantification of liver steatosis
  publication-title: Radiographics
– volume: 258
  start-page: 749
  year: 2011
  end-page: 759
  article-title: Estimation of hepatic proton‐density fat fraction by using MR imaging at 3.0 T
  publication-title: Radiology
– volume: 12
  start-page: 487
  year: 1994
  end-page: 495
  article-title: Quantification of liver fat using magnetic resonance spectroscopy
  publication-title: Magn Reson Imaging
– volume: 49
  start-page: 2055
  year: 2008
  end-page: 2062
  article-title: Composition of adipose tissue and marrow fat in humans by 1H NMR at 7 Tesla
  publication-title: J Lipid Res
– volume: 67
  start-page: 1684
  year: 2012
  end-page: 1693
  article-title: Three‐dimensional water/fat separation and T2* estimation based on whole‐image optimization—application in breathhold liver imaging at 1.5 T
  publication-title: Magn Reson Med
– volume: 18
  start-page: 337
  year: 2010
  end-page: 357, ix
  article-title: Quantification of liver fat with magnetic resonance imaging
  publication-title: Magn Reson Imaging Clin N Am
– volume: 24
  start-page: 784
  year: 2011
  end-page: 790
  article-title: In vivo characterization of the liver fat (1)H MR spectrum
  publication-title: NMR Biomed
– volume: 72
  start-page: 464
  year: 2014
  end-page: 470
  article-title: On the confounding effect of temperature on chemical shift‐encoded fat quantification
  publication-title: Magn Reson Med
– volume: 25
  start-page: 644
  year: 2007
  end-page: 652
  article-title: Water‐fat separation with IDEAL gradient‐echo imaging
  publication-title: J Magn Reson Imaging
– volume: 129
  start-page: 35
  year: 1997
  end-page: 43
  article-title: Improved method for accurate and efficient quantification of MRS data with use of prior knowledge
  publication-title: J Magn Reson
– volume: 72
  start-page: 1353
  year: 2014
  end-page: 1365
  article-title: Liver fat quantification using a multi‐step adaptive fitting approach with multi‐echo GRE imaging
  publication-title: Magn Reson Med
– volume: 29
  start-page: 1332
  year: 2009
  end-page: 1339
  article-title: Quantification of hepatic steatosis with MRI: the effects of accurate fat spectral modeling
  publication-title: J Magn Reson Imaging
– volume: 265
  start-page: 133
  year: 2012
  end-page: 142
  article-title: Effect of multipeak spectral modeling of fat for liver iron and fat quantification: correlation of biopsy with MR imaging results
  publication-title: Radiology
– volume: 33
  start-page: 873
  year: 2011
  end-page: 881
  article-title: T(1) independent, T(2) (*) corrected chemical shift based fat‐water separation with multi‐peak fat spectral modeling is an accurate and precise measure of hepatic steatosis
  publication-title: J Magn Reson Imaging
– volume: 18
  start-page: 841
  year: 2010
  end-page: 847
  article-title: Comparison of fat‐water MRI and single‐voxel MRS in the assessment of hepatic and pancreatic fat fractions in humans
  publication-title: Obesity (Silver Spring)
– volume: 60
  start-page: 1122
  year: 2008
  end-page: 1134
  article-title: Multiecho water‐fat separation and simultaneous R2* estimation with multifrequency fat spectrum modeling
  publication-title: Magn Reson Med
– volume: 38
  start-page: 619
  year: 2013
  end-page: 624
  article-title: Comparison of dixon and T1‐weighted MR methods to assess the degree of fat infiltration in duchenne muscular dystrophy patients
  publication-title: J Magn Reson Imaging
– volume: 59
  start-page: 382
  year: 2008
  end-page: 395
  article-title: Comparison of reconstruction accuracy and efficiency among autocalibrating data‐driven parallel imaging methods
  publication-title: Magn Reson Med
– volume: 36
  start-page: 1011
  year: 2012
  end-page: 1014
  article-title: Proton density fat‐fraction: a standardized MR‐based biomarker of tissue fat concentration
  publication-title: J Magn Reson Imaging
– volume: 251
  start-page: 67
  year: 2009
  end-page: 76
  article-title: Nonalcoholic fatty liver disease: diagnostic and fat‐grading accuracy of low‐flip‐angle multiecho gradient‐recalled‐echo MR imaging at 1.5 T
  publication-title: Radiology
– volume: 67
  start-page: 389
  year: 2012
  end-page: 404
  article-title: On the performance of T2* correction methods for quantification of hepatic fat content
  publication-title: Magn Reson Med
– volume: 67
  start-page: 638
  year: 2012
  end-page: 644
  article-title: Addressing phase errors in fat‐water imaging using a mixed magnitude/complex fitting method
  publication-title: Magn Reson Med
– volume: 258
  start-page: 767
  year: 2011
  end-page: 775
  article-title: Quantification of hepatic steatosis with T1‐independent, T2‐corrected MR imaging with spectral modeling of fat: blinded comparison with MR spectroscopy
  publication-title: Radiology
– volume: 66
  start-page: 199
  year: 2011
  end-page: 206
  article-title: Combination of complex‐based and magnitude‐based multiecho water‐fat separation for accurate quantification of fat‐fraction
  publication-title: Magn Reson Med
– volume: 54
  start-page: 636
  year: 2005
  end-page: 644
  article-title: Iterative decomposition of water and fat with echo asymmetry and least‐squares estimation (IDEAL): application with fast spin‐echo imaging
  publication-title: Magn Reson Med
– volume: 26
  start-page: 847
  year: 2008
  end-page: 850
  article-title: Optimal phased‐array combination for spectroscopy
  publication-title: Magn Reson Imaging
– volume: 26
  start-page: 347
  year: 2008
  end-page: 359
  article-title: Relaxation effects in the quantification of fat using gradient echo imaging
  publication-title: Magn Reson Imaging
– volume: 64
  start-page: 811
  year: 2010
  end-page: 822
  article-title: Chemical shift‐based water/fat separation: a comparison of signal models
  publication-title: Magn Reson Med
– ident: e_1_2_7_21_1
  doi: 10.1002/mrm.23044
– ident: e_1_2_7_33_1
  doi: 10.1002/mrm.22177
– ident: e_1_2_7_38_1
  doi: 10.1002/jmri.21682
– ident: e_1_2_7_20_1
– ident: e_1_2_7_31_1
  doi: 10.1002/mrm.21481
– ident: e_1_2_7_26_1
  doi: 10.1002/mrm.22840
– ident: e_1_2_7_24_1
  doi: 10.1194/jlr.D800010-JLR200
– ident: e_1_2_7_6_1
  doi: 10.1148/radiol.2511080666
– ident: e_1_2_7_16_1
  doi: 10.1016/j.mri.2007.08.012
– ident: e_1_2_7_28_1
  doi: 10.1002/mrm.23185
– ident: e_1_2_7_2_1
  doi: 10.1152/ajpendo.00064.2004
– ident: e_1_2_7_10_1
  doi: 10.1148/radiol.10100659
– ident: e_1_2_7_14_1
  doi: 10.1002/mrm.20624
– ident: e_1_2_7_36_1
  doi: 10.1007/BF02668096
– ident: e_1_2_7_12_1
  doi: 10.1148/radiol.12112520
– ident: e_1_2_7_22_1
  doi: 10.1002/jmri.21751
– ident: e_1_2_7_39_1
  doi: 10.1002/jmri.20831
– ident: e_1_2_7_11_1
  doi: 10.1002/mrm.25054
– ident: e_1_2_7_32_1
  doi: 10.1148/radiol.2303021331
– ident: e_1_2_7_23_1
  doi: 10.1002/nbm.1622
– ident: e_1_2_7_17_1
  doi: 10.1002/mrm.23016
– ident: e_1_2_7_13_1
  doi: 10.1002/jmri.23741
– ident: e_1_2_7_18_1
  doi: 10.1002/mrm.21737
– ident: e_1_2_7_41_1
– ident: e_1_2_7_7_1
  doi: 10.1148/radiol.10100708
– ident: e_1_2_7_35_1
  doi: 10.1016/j.mri.2008.01.050
– ident: e_1_2_7_37_1
  doi: 10.1006/jmre.1997.1244
– ident: e_1_2_7_4_1
  doi: 10.1381/0960892053576596
– volume: 9
  start-page: 291
  year: 2005
  ident: e_1_2_7_3_1
  article-title: Steatosis and hepatocellular carcinoma risk
  publication-title: Eur Rev Med Pharmacol Sci
– ident: e_1_2_7_25_1
  doi: 10.1002/jmri.23835
– ident: e_1_2_7_27_1
  doi: 10.1002/jmri.23998
– ident: e_1_2_7_5_1
  doi: 10.1016/0730-725X(94)92543-7
– ident: e_1_2_7_15_1
  doi: 10.1002/mrm.21301
– ident: e_1_2_7_30_1
  doi: 10.1097/RLI.0b013e3181862413
– ident: e_1_2_7_40_1
  doi: 10.1002/mrm.24951
– ident: e_1_2_7_9_1
  doi: 10.1016/j.mric.2010.08.013
– ident: e_1_2_7_8_1
  doi: 10.1002/jmri.22514
– ident: e_1_2_7_34_1
  doi: 10.1148/rg.291075123
– ident: e_1_2_7_19_1
  doi: 10.1002/mrm.22455
– ident: e_1_2_7_29_1
  doi: 10.1038/oby.2009.352
– reference: 18486392 - Magn Reson Imaging. 2008 Jul;26(6):847-50
– reference: 19002057 - Invest Radiol. 2008 Dec;43(12):854-60
– reference: 19221054 - Radiology. 2009 Apr;251(1):67-76
– reference: 22777847 - J Magn Reson Imaging. 2012 Nov;36(5):1011-4
– reference: 18093781 - Magn Reson Imaging. 2008 Apr;26(3):347-59
– reference: 21713978 - Magn Reson Med. 2012 Mar;67(3):638-44
– reference: 21695724 - Magn Reson Med. 2011 Jul;66(1):199-206
– reference: 16231592 - Eur Rev Med Pharmacol Sci. 2005 Sep-Oct;9(5):291-3
– reference: 16092103 - Magn Reson Med. 2005 Sep;54(3):636-44
– reference: 19472390 - J Magn Reson Imaging. 2009 Jun;29(6):1332-9
– reference: 21448952 - J Magn Reson Imaging. 2011 Apr;33(4):873-81
– reference: 17326087 - J Magn Reson Imaging. 2007 Mar;25(3):644-52
– reference: 20593375 - Magn Reson Med. 2010 Sep;64(3):811-22
– reference: 18228603 - Magn Reson Med. 2008 Feb;59(2):382-95
– reference: 21248233 - Radiology. 2011 Mar;258(3):767-75
– reference: 24123362 - Magn Reson Med. 2014 Aug;72(2):464-70
– reference: 14990831 - Radiology. 2004 Mar;230(3):652-9
– reference: 18509197 - J Lipid Res. 2008 Sep;49(9):2055-62
– reference: 21834002 - NMR Biomed. 2011 Aug;24(7):784-90
– reference: 9405214 - J Magn Reson. 1997 Nov;129(1):35-43
– reference: 15826485 - Obes Surg. 2005 Mar;15(3):442-6
– reference: 8007779 - Magn Reson Imaging. 1994;12(3):487-95
– reference: 19168847 - Radiographics. 2009 Jan-Feb;29(1):231-60
– reference: 19243059 - J Magn Reson Imaging. 2009 Mar;29(3):629-35
– reference: 23165934 - J Magn Reson Imaging. 2013 Feb;37(2):414-22
– reference: 18956464 - Magn Reson Med. 2008 Nov;60(5):1122-34
– reference: 17654578 - Magn Reson Med. 2007 Aug;58(2):354-64
– reference: 22923718 - Radiology. 2012 Oct;265(1):133-42
– reference: 21094444 - Magn Reson Imaging Clin N Am. 2010 Aug;18(3):337-57, ix
– reference: 21661045 - Magn Reson Med. 2012 Feb;67(2):389-404
– reference: 15339742 - Am J Physiol Endocrinol Metab. 2005 Feb;288(2):E462-8
– reference: 19859956 - Magn Reson Med. 2010 Jan;63(1):79-90
– reference: 24323332 - Magn Reson Med. 2014 Nov;72(5):1353-65
– reference: 11390270 - MAGMA. 2001 May;12(2-3):141-52
– reference: 22189760 - Magn Reson Med. 2012 Jun;67(6):1684-93
– reference: 21212366 - Radiology. 2011 Mar;258(3):749-59
– reference: 23292884 - J Magn Reson Imaging. 2013 Sep;38(3):619-24
SSID ssj0009974
Score 2.4160564
Snippet Purpose To evaluate the impact of different fat spectral models on proton density fat fraction quantification using chemical shift‐encoded MRI (CSE‐MRI)....
To evaluate the impact of different fat spectral models on proton density fat fraction quantification using chemical shift-encoded MRI (CSE-MRI). In a...
Purpose To evaluate the impact of different fat spectral models on proton density fat fraction quantification using chemical shift-encoded MRI (CSE-MRI)....
PurposeTo evaluate the impact of different fat spectral models on proton density fat fraction quantification using chemical shift‐encoded MRI...
PURPOSETo evaluate the impact of different fat spectral models on proton density fat fraction quantification using chemical shift-encoded MRI...
SourceID pubmedcentral
proquest
pubmed
crossref
wiley
istex
SourceType Open Access Repository
Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 845
SubjectTerms Calibration
Chemical equilibrium
Computer Simulation
fat quantification
fat spectrum
Fatty Liver - pathology
Humans
Image Processing, Computer-Assisted
In vivo methods and tests
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Magnetic Resonance Spectroscopy
nonalcoholic fatty liver disease
Proton density (concentration)
proton density fat fraction
Protons
Sensitivity and Specificity
Spectra
spectral model of fat
Title Sensitivity of chemical shift-encoded fat quantification to calibration of fat MR spectrum
URI https://api.istex.fr/ark:/67375/WNG-6LC46C9B-J/fulltext.pdf
https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fmrm.25681
https://www.ncbi.nlm.nih.gov/pubmed/25845713
https://www.proquest.com/docview/1757596320
https://www.proquest.com/docview/2509218867
https://www.proquest.com/docview/1760883157
https://www.proquest.com/docview/1776658273
https://pubmed.ncbi.nlm.nih.gov/PMC4592785
Volume 75
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwELaqIhAXHgVKoCCDEOKSbRK_YnGCFaWqSA8LFT1UipzEVquyWdjNSogTP4HfyC9hxnmUhQUhbok8juLxzPhLPP6GkCcqMbE2AjyNlzbkVsowrUQcFojtNXQpOR5wzg7l_hE_OBbHG-R5fxam5YcYfrihZ_h4jQ5uisXuBWnodD4dJUifBfEXc7UQEE0uqKO0bhmYFcc4o3nPKhQlu0PPlbXoEqr18zqg-Xu-5M841i9Ee9fJST-ENv_kfLRsilH55Rd2x_8c4w1yrQOo9EVrUTfJhq23yJWs24LfIpd9zmi5uEVO3mLye1t9gs4cLTvuAbo4PXPN96_fkCOzshV1pqGflqZNS_KWQJsZBUl8N38LvVEom1B_8nO-nN4mR3uv3o33w65YQ1hKgBWhc1JzpytXRBWWUdcRczKpIidTxiLjJGMmSq0qmEl5FSvFLOeJkrYShpfasDtks57V9i6hRhnc_JSl44zrChnkrYsLBV_QkdHcBORZP2152TGZY0GND3nLwZzkoLfc6y0gjwfRjy19xzqhp37uBwkzP8d8NyXy94evc_lmzOVYv8wPArLTG0feufoiB_ylBISxJFrbDBBTA4xKpQrIo6EZfBg3ZkxtZ0t8BKgwZbH4q4ySgBYBbQZkuzXH4X0TQJFCxdCiVgx1EEAO8dWW-uzUc4lzoROVClCpt8M_KynPJpm_uPfvovfJVcCXXZL7DtkE87EPAMM1xUPvrD8AxfdDQg
linkProvider Wiley-Blackwell
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB6VVlAuPAqFhQIBIcQl2zwcO5a4wIqylM0ellbtAWQ5ia1WZXdhHxLixE_gN_JLmHGyKQsLQtwSeRzF4xn7sz3-BuCxiHQodYKexgrjM8O5n5ZJ6OeE7SVWKRhdcM76vHvI9o-T4zV4trgLU_FDNBtu5BluvCYHpw3p3XPW0OFk2I6IP-sCbFBGb7egGpyTR0lZcTALRiONZAteoSDabaouzUYbpNjPq6Dm7xGTPyNZNxXtXYX3i0ZUEShn7fksbxdffuF3_N9WXoMrNUb1nldGdR3WzGgLLmX1KfwWXHRho8X0Brx7S_HvVQIKb2y9oqYf8KYnp3b2_es3osksTelZPfM-zXUVmeSMwZuNPZSkn3OvWJuEsoHnLn9O5sObcLj38qDT9et8DX7BEVn41nLJrCxtHpSUSV0GseVRGViexnGgLY9jHaRG5LFOWRkKERvGIsFNmWhWSB1vw_poPDK3wdNC0_knLyyLmSyJRN7YMBe4iA60ZLoFTxf9poqazJxyanxQFQ1zpFBvyumtBY8a0Y8Vg8cqoSeu8xsJPTmjkDeRqKP-K8V7HcY78oXab8HOwjpU7e1ThRBMJDiSRcHKYkSZEpFUykULHjbF6MZ0NqNHZjynT6AK0zhM_iojOAJGBJwtuFXZY_O_EQLJRIRYIpYstREgGvHlktHpiaMTZ4mMRJqgSp0h_llJKhtk7uHOv4s-gM3uQdZTvdf9N3fhMsLNOuZ9B9bRlMw9hHSz_L7z3B_K9Udd
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB6VVlRceJRHAwUCQohLtnk4dixOsGUppVmhhYoeKllOYqtVu9mym5UQJ34Cv5Ffwth5lIUFIW6JZhzF4xn7Szz-BuAJC2XAZYyRRnLlEUWplxRx4GUG23NskhNzwDkd0t0DsncYH67A8_YsTM0P0f1wM5Fh52sT4OeF3r4gDR1Px73Q0GddgjVC_cS49M7ogjuK85qCmREz0XDS0gr54XbXdGExWjN2_bwMaf6eMPkzkLUr0eAaHLV9qBNQTnvzKuvlX36hd_zPTl6Hqw1CdV_ULnUDVlS5Aetpswe_AZdt0mg-uwlH7032e11-wp1oN2_IB9zZ8Ymuvn_9ZkgyC1W4Wlbup7ms85KsK7jVxEVN8272FlsbpXTk2qOf0_n4FhwMXn3o73pNtQYvR-sHntaUE80LnfmFqaPO_UjTsPA1TaLIl5pGkfQTxbJIJqQIGIsUISGjqoglybmMbsNqOSnVJriSSbP7SXNNIsILQyGvdJAx_IT2JSfSgWftsIm8oTI3FTXORE3CHAq0m7B2c-Bxp3pe83csU3pqx77TkNNTk_DGYvFx-FrQ_T6hff5S7Dmw1TqHaGJ9JhCAsRjnsdBfKkaMyRFHJZQ58KgTYxCbnRlZqsncPAJNmERB_FcdRhEuItx04E7tjt37hggjYxaghC04aqdgSMQXJeXJsSUTJzEPWRKjSa0f_tlIIh2l9uLuv6s-hPV3OwOx_2b49h5cQazZJLxvwSp6krqPeK7KHti4_QHkrkYV
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Sensitivity+of+chemical+shift%E2%80%90encoded+fat+quantification+to+calibration+of+fat+MR+spectrum&rft.jtitle=Magnetic+resonance+in+medicine&rft.au=Wang%2C+Xiaoke&rft.au=Hernando%2C+Diego&rft.au=Reeder%2C+Scott+B.&rft.date=2016-02-01&rft.issn=0740-3194&rft.eissn=1522-2594&rft.volume=75&rft.issue=2&rft.spage=845&rft.epage=851&rft_id=info:doi/10.1002%2Fmrm.25681&rft.externalDBID=n%2Fa&rft.externalDocID=10_1002_mrm_25681
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0740-3194&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0740-3194&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0740-3194&client=summon