Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Medical Imaging
  4. Volume 20, Issue 1
  5. Article
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Purpose

The objective of this study was to evaluate the robustness of proton density fat fraction (PDFF) data determined by magnetic resonance imaging (MRI) and spectroscopy (MRS) spatially resolved error estimation.

Materials and Methods

Using standard T2* relaxation time measurement protocols, and MRI data with water and fat nominally in phase or out of phase relative to each other were acquired on a 7 T small animal scanner. Based on a total of 24 different echo times, PDFF maps were calculated in a magnitude-based approach. After identification of the decisive error-prone variables, pixel-wise error estimation was performed by simple propagation of uncertainty. The method was then used to evaluate PDFF data acquired for an explanted mouse liver and an mouse liver measurement.

Results

The determined error maps helped excluding measurement errors as cause of unexpected local PDFF variations in the explanted liver. For measurements, severe error maps gave rise to doubts in the acquired PDFF maps and triggered an in-depth analysis of possible causes, yielding abdominal movement or bladder filling as occurring reasons for the increased errors.

Conclusion

The combination of pixel-wise acquisition of PDFF data and the corresponding error maps allows for a more specific, spatially resolved evaluation of the PDFF value reliability.

© 2024 Polei et al.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Loading

Article metrics loading...

/content/journals/cmir/10.2174/0115734056263741231117112245
2024-01-01
2025-07-10

  • From This Site

    /content/journals/cmir/10.2174/0115734056263741231117112245
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
The full text of this item is not currently available.

References

  1. LuyckxV.A. BrennerB.M. Birth weight, malnutrition and kidney-associated outcomes—a global concern.Nat. Rev. Nephrol.201511313514910.1038/nrneph.2014.25125599618
     [Google Scholar]
  2. AndersonA.S. KeyT.J. NoratT. European Code against Cancer 4th Edition: Obesity, body fatness and cancer.Cancer Epidemiol201539S1S34S45
     [Google Scholar]
  3. Global status report on noncommunicable diseases 2014.2014Available from: https://www.who.int/publications/i/item/9789241564854
  4. HuF.B. StampferM.J. MansonJ.E. AscherioA. ColditzG.A. SpeizerF.E. HennekensC.H. WillettW.C. Dietary saturated fats and their food sources in relation to the risk of coronary heart disease in women.Am. J. Clin. Nutr.19997061001100810.1093/ajcn/70.6.100110584044
     [Google Scholar]
  5. KelemenL.E. KushiL.H. JacobsD.R.Jr CerhanJ.R. Associations of dietary protein with disease and mortality in a prospective study of postmenopausal women.Am. J. Epidemiol.2005161323924910.1093/aje/kwi03815671256
     [Google Scholar]
  6. NigroD. MenottiF. CentoA.S. SerpeL. ChiazzaF. Dal BelloF. RomanielloF. MedanaC. CollinoM. AragnoM. MastrocolaR. Chronic administration of saturated fats and fructose differently affect SREBP activity resulting in different modulation of Nrf2 and Nlrp3 inflammasome pathways in mice liver.J. Nutr. Biochem.20174216017110.1016/j.jnutbio.2017.01.01028189916
     [Google Scholar]
  7. ThanN.N. NewsomeP.N. A concise review of non-alcoholic fatty liver disease.Atherosclerosis2015239119220210.1016/j.atherosclerosis.2015.01.00125617860
     [Google Scholar]
  8. ReederS.B. SirlinC.B. Quantification of liver fat with magnetic resonance imaging.Magn. Reson. Imaging Clin. N. Am.2010183337357, ix10.1016/j.mric.2010.08.01321094444
     [Google Scholar]
  9. BannasP. KramerH. HernandoD. AgniR. CunninghamA.M. MandalR. MotosugiU. SharmaS.D. Munoz del RioA. FernandezL. ReederS.B. Quantitative magnetic resonance imaging of hepatic steatosis: Validation in ex vivo human livers.Hepatology20156251444145510.1002/hep.2801226224591
     [Google Scholar]
  10. SimchickG YinA YinH Fat spectral modeling on triglyceride composition quantification using chemical shift encoded magnetic resonance imaging.Magn Reson Imaging.201852849310.1016/j.mri.2018.06.012
     [Google Scholar]
  11. RuschkeS. PokorneyA. BaumT. EggersH. MillerJ.H. HuH.H. KarampinosD.C. Measurement of vertebral bone marrow proton density fat fraction in children using quantitative water–fat MRI.MAGMA201730544946010.1007/s10334‑017‑0617‑028382554
     [Google Scholar]
  12. HernandoD. SharmaS.D. Aliyari GhasabehM. AlvisB.D. AroraS.S. HamiltonG. PanL. ShafferJ.M. SofueK. SzeverenyiN.M. WelchE.B. YuanQ. BashirM.R. KamelI.R. RiceM.J. SirlinC.B. YokooT. ReederS.B. Multisite, multivendor validation of the accuracy and reproducibility of proton-density fat-fraction quantification at 1.5T and 3T using a fat-water phantom.Magn. Reson. Med.20177741516152410.1002/mrm.2622827080068
     [Google Scholar]
  13. MeisamyS. HinesC.D.G. HamiltonG. SirlinC.B. McKenzieC.A. YuH. BrittainJ.H. ReederS.B. Quantification of hepatic steatosis with T1-independent, T2-corrected MR imaging with spectral modeling of fat: blinded comparison with MR spectroscopy.Radiology2011258376777510.1148/radiol.1010070821248233
     [Google Scholar]
  14. TangA. DesaiA. HamiltonG. WolfsonT. GamstA. LamJ. ClarkL. HookerJ. ChavezT. AngB.D. MiddletonM.S. PetersonM. LoombaR. SirlinC.B. Accuracy of MR imaging-estimated proton density fat fraction for classification of dichotomized histologic steatosis grades in nonalcoholic fatty liver disease.Radiology2015274241642510.1148/radiol.1414075425247408
     [Google Scholar]
  15. YuH. ShimakawaA. McKenzieC.A. BrodskyE. BrittainJ.H. ReederS.B. Multiecho water-fat separation and simultaneous R estimation with multifrequency fat spectrum modeling.Magn. Reson. Med.20086051122113410.1002/mrm.2173718956464
     [Google Scholar]
  16. LiuC.Y. McKenzieC.A. YuH. BrittainJ.H. ReederS.B. Fat quantification with IDEAL gradient echo imaging: Correction of bias from T 1 and noise.Magn. Reson. Med.200758235436410.1002/mrm.2130117654578
     [Google Scholar]
  17. HinesC.D.G. YuH. ShimakawaA. McKenzieC.A. BrittainJ.H. ReederS.B. T 1 independent, T 2 * corrected MRI with accurate spectral modeling for quantification of fat: Validation in a fat-water-SPIO phantom.J. Magn. Reson. Imaging20093051215122210.1002/jmri.2195719856457
     [Google Scholar]
  18. YuH. ShimakawaA. McKenzieC.A. LuW. ReederS.B. HinksR.S. BrittainJ.H. Phase and amplitude correction for multi-echo water–fat separation with bipolar acquisitions.J. Magn. Reson. Imaging20103151264127110.1002/jmri.2211120432366
     [Google Scholar]
  19. WenZ. ReederS.B. PinedaA.R. PelcN.J. Noise considerations of three-point water-fat separation imaging methods.Med. Phys.20083583597360610.1118/1.295264418777920
     [Google Scholar]
  20. YuH. ShimakawaA. HinesC.D.G. McKenzieC.A. HamiltonG. SirlinC.B. BrittainJ.H. ReederS.B. Combination of complex-based and magnitude-based multiecho water-fat separation for accurate quantification of fat-fraction.Magn. Reson. Med.201166119920610.1002/mrm.2284021695724
     [Google Scholar]
  21. MahlkeC. HernandoD. JahnC. CiglianoA. IttermannT. MösslerA. KromreyM.L. DomaskaG. ReederS.B. KühnJ.P. Quantification of liver proton-density fat fraction in 7.1T preclinical MR systems: Impact of the fitting technique.J. Magn. Reson. Imaging20164461425143110.1002/jmri.2531927197806
     [Google Scholar]
  22. NyquistH. Certain topics in telegraph transmission theory.Trans. AIEE1928472617644
     [Google Scholar]
  23. TkácI. StarcukZ. ChoiI.Y. GruetterR. In vivo 1H NMR spectroscopy of rat brain at 1 ms echo time.Magn. Reson. Med.199941464965610.1002/(SICI)1522‑2594(199904)41:4<649::AID‑MRM2>3.0.CO;2‑G10332839
     [Google Scholar]
  24. ZhongX. NickelM.D. KannengiesserS.A.R. DaleB.M. KieferB. BashirM.R. Liver fat quantification using a multi-step adaptive fitting approach with multi-echo GRE imaging.Magn. Reson. Med.20147251353136510.1002/mrm.2505424323332
     [Google Scholar]
  25. HernandoD. HinesC.D.G. YuH. ReederS.B. Addressing phase errors in fat-water imaging using a mixed magnitude/complex fitting method.Magn. Reson. Med.201267363864410.1002/mrm.2304421713978
     [Google Scholar]
  26. WangX. HernandoD. ReederS.B. Sensitivity of chemical shift-encoded fat quantification to calibration of fat MR spectrum.Magn. Reson. Med.201675284585110.1002/mrm.2568125845713
     [Google Scholar]
  27. FujiiM. ShibazakiY. WakamatsuK. HondaY. KawauchiY. SuzukiK. ArumugamS. WatanabeK. IchidaT. AsakuraH. YoneyamaH. A murine model for non-alcoholic steatohepatitis showing evidence of association between diabetes and hepatocellular carcinoma.Med. Mol. Morphol.201346314115210.1007/s00795‑013‑0016‑123430399
     [Google Scholar]
  28. ReederSB CruiteI HamiltonG Quantitative assessment of liver fat with magnetic resonance imaging and spectroscopy.J Magn Reson Imaging2011344729749
     [Google Scholar]
  29. RyuJ.E. JoW. ChoiH.J. JangS. LeeH.J. WooD.C. KimJ.K. KimK.W. YuE.S. SonW.C. Evaluation of nonalcoholic fatty liver disease in C57BL/6J mice by using MRI and histopathologic analyses.Comp. Med.201565540941526473344
     [Google Scholar]
  30. RatziuV. CharlotteF. HeurtierA. GombertS. GiralP. BruckertE. GrimaldiA. CapronF. PoynardT. Sampling variability of liver biopsy in nonalcoholic fatty liver disease.Gastroenterology200512871898190610.1053/j.gastro.2005.03.08415940625
     [Google Scholar]
  31. KangG.H. CruiteI. ShiehmortezaM. WolfsonT. GamstA.C. HamiltonG. BydderM. MiddletonM.S. SirlinC.B. Reproducibility of MRI-determined proton density fat fraction across two different MR scanner platforms.J. Magn. Reson. Imaging201134492893410.1002/jmri.2270121769986
     [Google Scholar]
  32. NoureddinM. LamJ. PetersonM.R. MiddletonM. HamiltonG. LeT.A. BettencourtR. ChangchienC. BrennerD.A. SirlinC. LoombaR. Utility of magnetic resonance imaging versus histology for quantifying changes in liver fat in nonalcoholic fatty liver disease trials.Hepatology20135861930194010.1002/hep.2645523696515
     [Google Scholar]
  33. PetersonP. MånssonS. Fat quantification using multiecho sequences with bipolar gradients: Investigation of accuracy and noise performance.Magn. Reson. Med.201471121922910.1002/mrm.2465723412971
     [Google Scholar]
  34. HaufeW.M. WolfsonT. HookerC.A. HookerJ.C. CovarrubiasY. SchleinA.N. HamiltonG. MiddletonM.S. AngelesJ.E. HernandoD. ReederS.B. SchwimmerJ.B. SirlinC.B. Accuracy of PDFF estimation by magnitude-based and complex-based MRI in children with MR spectroscopy as a reference.J. Magn. Reson. Imaging20174661641164710.1002/jmri.2569928323377
     [Google Scholar]
  35. MiddletonM.S. HaufeW. HookerJ. BorgaM. Dahlqvist LeinhardO. RomuT. TunónP. HamiltonG. WolfsonT. GamstA. LoombaR. SirlinC.B. Quantifying Abdominal Adipose Tissue and Thigh Muscle Volume and Hepatic Proton Density Fat Fraction: Repeatability and Accuracy of an MR Imaging–based, Semiautomated Analysis Method.Radiology2017283243844910.1148/radiol.201716060628278002
     [Google Scholar]
  36. InsullW.Jr The pathology of atherosclerosis: plaque development and plaque responses to medical treatment.Am. J. Med.2009122S1S3S1410.1016/j.amjmed.2008.10.01319110086
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056263741231117112245
Loading
7 Tesla MRI Liver Fat Quantification in Mice: Data Quality Assessment
Curr. Med. Imaging 20, e15734056263741 (2024); https://doi.org/10.2174/0115734056263741231117112245
/content/journals/cmir/10.2174/0115734056263741231117112245
/content/journals/cmir/10.2174/0115734056263741231117112245
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Data quality; Liver fat quantification; MRI; NAFLD; PDFF; Small animal

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test