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...
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Published in | Magnetic resonance in medicine Vol. 75; no. 2; pp. 845 - 851 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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United States
Blackwell Publishing Ltd
01.02.2016
Wiley Subscription Services, Inc |
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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. |
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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 |
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Keywords | fat spectrum fat quantification magnetic resonance imaging nonalcoholic fatty liver disease proton density fat fraction spectral model of fat |
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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 |
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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... |
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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 |
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