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Accuracy of fat fraction estimation using Dixon: experimental phantom study

https://doi.org/10.24835/1607-0763-1160

Abstract

Objective. Quantitative assessment of Dixon two-point and three-point technologies operation using phantom modeling in the range from 0 to 70%.

Materials and methods. To simulate substances with different concentrations of the fat phase we chose direct oil-in-water emulsions. Tubes with ready-made emulsions were placed in a phantom. Emulsions based on vegetable oils were presented in the range from 0–70%. The phantom was scanned on an Optima MR450w MRI tomograph (GE, USA) in two Dixon modes: the accelerated two-point method “Lava-Flex” and the three-point method “IDEAL IQ”. A scan was performed on a GEM Flex LG Full RF coil. We calculated fat fraction (FF) using two formulas.

Results. There is a linear relationship of the determined values when calculating the fat concentration in “IDEAL IQ” mode and using the formula based on Water and Fat. The accuracy of body fat percentage measurement in “IDEAL IQ” mode is higher than in “Lava-Flex” mode. According to the MR-sequence “Lava-Flex” draws attention to the overestimation of the measured values of the concentration of fat in relation to the specified values by an average of 57.6% over the entire range, with an average absolute difference of 17.2%.

Conclusion. Using the “IDEAL IQ” sequence, the results of the quantitative determination of fractions by formulas were demonstrated, which are more consistent with the specified values in the phantom. In order to correctly quantify the fat fraction, it is preferable to calculate from the Water and Fat images using Equation 2. Calculations from the In-phase and Out-phase images provide ambiguous results. Phantom modeling with direct emulsions allowed us to detect the shift of the measured fat fraction.

About the Authors

O. Yu. Panina
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of Moscow Health Care Department; A.I. Evdokimov Moscow State University of Medicine and Dentistry of the Ministry of Healthcare of the Russian Federation; City Clinical Oncology Hospital No. 1
Russian Federation

Olga Yu. Panina – Junior Scientist Researcher of Technical Monitoring and QA Development Department, Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of Moscow Health Care Department; MD, radiologist, City Clinical Oncological Hospital No. 1 of Moscow Health Care Department; senior laboratory assistant, Moscow State University of Medicine and Dentistry named after A.I. Evdokimov

24, Petrovka str., Moscow 127051;
20/1, Delegatskaya str., Moscow, 127473;
17/1, Baumanskaya str., Moscow 105005



A. I. Gromov
A.I. Evdokimov Moscow State University of Medicine and Dentistry of the Ministry of Healthcare of the Russian Federation
Russian Federation

Alexander I. Gromov – Doct. of Sci. (Med.), Associate Professor; head of the radiation diagnosis and treatment methods, Oncourology Department

20/1, Delegatskaya str., Moscow, 127473



E. S. Akhmad
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of Moscow Health Care Department
Russian Federation

Ekaterina S. Akhmad – Scientist Researcher of Technical Monitoring and QA Development

24, Petrovka str., Moscow 127051



A. V. Petraikin
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of Moscow Health Care Department
Russian Federation

Alexey V. Petraikin – Cand. of Sci. (Med.), Associate Professor, Senior Researcher of Technical Monitoring and QA Development

24, Petrovka str., Moscow 127051



D. A. Bogachev
Company with limited liability “EmulCom”
Russian Federation

Dmitry A. Bogachev – Head of the laboratory

Moscow region



D. S. Semenov
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of Moscow Health Care Department
Russian Federation

Dmitry S. Semenov – Scientist Researcher of Technical Monitoring and QA Development

24, Petrovka str., Moscow 127051



A. V. Vladzymyrskyy
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of Moscow Health Care Department
Russian Federation

Anton V. Vladzymyrskyy – Doct. of Sci. (Med.), Associate Professor, Deputy Director for Science

24, Petrovka str., Moscow 127051



Yu. A. Vasilev
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of Moscow Health Care Department
Russian Federation

Yury A. Vasilev – Cand. of Sci. (Med.), Director

24, Petrovka str., Moscow 127051



References

1. Van Vucht N., Santiago R., Lottmann B. et al. The Dixon technique for MRI of the bone marrow. Skeletal Radiol. 2019; 48 (12): 1861–1874. https://doi.org/10.1007/s00256-019-03271-4.

2. Gromov A.I., Gorinov A.V., Galljamov E.A. Mesenteric chillous lymphangioma. Visualization features on opposedphase MR images. Medical Visualization. 2019; 23 (4): 86–92. https://doi.org/10.24835/1607-0763-2019-4-86-92 (In Russian)

3. Dixon W.T. Simple proton spectroscopic imaging. Radiology. 1984; 153. https://doi.org/10.1148/radiology.153.1.6089263

4. Outwater E.K., Blasbalg R., Siegelman E.S., Vala M. Detection of Lipid in Abdominal Tissues with Opposed-Phase Gradient-Echo Images at 1.5 T: Techniques and Diagnostic Importance. Radiographics. 1998; 18. https://doi.org/10.1148/radiographics.18.6.9821195

5. Serai S.D., Dillman J.R., Trout A.T. Proton density fat fraction measurements at 1.5- and 3-T hepatic MR imaging: Same-day agreement among readers and across two imager manufacturers. Radiology. 2017; 284. https://doi.org/10.1148/radiol.2017161786

6. Schmeel F.C., Vomweg T., Träber F. et al. Proton density fat fraction MRI of vertebral bone marrow: Accuracy, repeatability, and reproducibility among readers, field strengths, and imaging platforms. J. Magn. Reson. Imaging. 2019; 50. https://doi.org/10.1002/jmri.26748

7. Lohöfer F.K., Kaissis G.A., Müller-Leisse C. et al. Acceleration of chemical shift encoding-based water fat MRI for liver proton density fat fraction and T2 mapping using compressed sensing. PLoS One. 2019; 14. https://doi.org/10.1371/journal.pone.0224988

8. Reeder S.B., Hu H.H., Sirlin C.B. Proton density fat-fraction: A standardized mr-based biomarker of tissue fat concentration. J. Magn. Reson. Imaging. 2012; 36. https://doi.org/10.1002/jmri.23741

9. Fischer M.A., Pfirrmann C.W.A., Espinosa N. et al. Dixon-based MRI for assessment of muscle-fat content in phantoms, healthy volunteers and patients with achillodynia: Comparison to visual assessment of calf muscle quality. Eur. Radiol. 2014; 24: 1366–1375. https://doi.org/10.1007/s00330-014-3121-1

10. Bainbridge A., Bray T.J.P., Sengupta R., Hall-Craggs M.A. Practical Approaches to Bone Marrow Fat Fraction Quantification Across Magnetic Resonance Imaging Platforms. J. Magn. Reson. Imaging. 2020; 52: 298–306. https://doi.org/10.1002/jmri.27039

11. Hernando D., Sharma S.D., Aliyari Ghasabeh M. et al. 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. 2017; 77: 1516–1524. https://doi.org/10.1002/mrm.26228

12. Sergunova K.A. The use of reverse emulsion based on siloxanes to control the measured diffusion coefficient in magnetic resonance imaging. Biomedical Engineering. 2019; 5: 22–25. (In Russian)

13. Morozov S., Sergunova K., Petraikin A. et al. Diffusion processes modeling in magnetic resonance imaging. Insights Imaging. 2020; 11. https://doi.org/10.1186/s13244-020-00863-w

14. Bhat V., Velandai S., Belliappa V. et al. Quantification of Liver Fat with mDIXON Magnetic Resonance Imaging, Comparison with the Computed Tomography and the Biopsy. J. Clin. DIAGNOSTIC. Res. 2017;11:TC06.

15. Samji K., Alrashed A., Shabana W.M. et al. Comparison of high-resolution T1W 3D GRE (LAVA) with 2-point Dixon fat/ water separation (FLEX) to T1W fast spin echo (FSE) in prostate cancer (PCa). Clin. Imaging. 2016; 40. https://doi.org/10.1016/j.clinimag.2015.11.023

16. Reeder S.B., Pineda A.R., Wen Z. et al. 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. https://doi.org/10.1002/mrm.20624

17. Labranche R., Gilbert G., Cerny M. et al. Liver iron quantification with MR imaging: A primer for radiologists. Radiographics. 2018; 38. https://doi.org/10.1148/rg.2018170079

18. Hayashi T., Fukuzawa K., Yamazaki H. et al. Multicenter, multivendor phantom study to validate proton density fat fraction and T2* values calculated using vendor-provided 6-point DIXON methods. Clin. Imaging. 2018; 51: 38–42. https://doi.org/10.1016/j.clinimag.2018.01.011

19. Hutton C., Gyngell M.L., Milanesi M. et al. Validation of a standardized MRI method for liver fat and T2 quantification. PLoS One. 2018; 13. https://doi.org/10.1371/journal.pone.0204175


Review

For citations:


Panina O.Yu., Gromov A.I., Akhmad E.S., Petraikin A.V., Bogachev D.A., Semenov D.S., Vladzymyrskyy A.V., Vasilev Yu.A. Accuracy of fat fraction estimation using Dixon: experimental phantom study. Medical Visualization. 2022;26(4):147-158. https://doi.org/10.24835/1607-0763-1160

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ISSN 1607-0763 (Print)
ISSN 2408-9516 (Online)