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Journal articles on the topic 'Water-fat MRI'

Author: Grafiati
Published: 25 June 2021
Last updated: 15 February 2022

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1

Schick, Fritz. "Fat and water selective MRI." Zeitschrift für Medizinische Physik 27, no. 1 (March 2017): 1–3. http://dx.doi.org/10.1016/j.zemedi.2017.01.003.

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2

Vasanawala, Shreyas S., Ananth J. Madhuranthakam, Ramesh Venkatesan, Arvind Sonik, Peng Lai, and Anja C. S. Brau. "Volumetric fat-water separated T2-weighted MRI." Pediatric Radiology 41, no. 7 (January 18, 2011): 875–83. http://dx.doi.org/10.1007/s00247-010-1963-5.

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3

Taviani, Valentina, Diego Hernando, Christopher J. Francois, Ann Shimakawa, Karl K. Vigen, Scott K. Nagle, Mark L. Schiebler, Thomas M. Grist, and Scott B. Reeder. "Whole-heart chemical shift encoded water-fat MRI." Magnetic Resonance in Medicine 72, no. 3 (November 1, 2013): 718–25. http://dx.doi.org/10.1002/mrm.24982.

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4

Andersson, Thord, Thobias Romu, Anette Karlsson, Bengt Norén, Mikael F. Forsgren, Örjan Smedby, Stergios Kechagias, et al. "Consistent intensity inhomogeneity correction in water-fat MRI." Journal of Magnetic Resonance Imaging 42, no. 2 (October 30, 2014): 468–76. http://dx.doi.org/10.1002/jmri.24778.

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5

Jacob, M., and B. P. Sutton. "Algebraic Decomposition of Fat and Water in MRI." IEEE Transactions on Medical Imaging 28, no. 2 (February 2009): 173–84. http://dx.doi.org/10.1109/tmi.2008.927344.

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6

Picaud, Julien, Guylaine Collewet, and Jérôme Idier. "Quantification of mass fat fraction in fish using water–fat separation MRI." Magnetic Resonance Imaging 34, no. 1 (January 2016): 44–50. http://dx.doi.org/10.1016/j.mri.2015.10.004.

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7

Salvati, Roberto, Eric Hitti, Jean-Jacques Bellanger, Hervé Saint-Jalmes, and Giulio Gambarota. "Fat ViP MRI: Virtual Phantom Magnetic Resonance Imaging of water–fat systems." Magnetic Resonance Imaging 34, no. 5 (June 2016): 617–23. http://dx.doi.org/10.1016/j.mri.2015.12.002.

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8

Joshi, Anand A., Houchun H. Hu, Richard M. Leahy, Michael I. Goran, and Krishna S. Nayak. "Automatic intra-subject registration-based segmentation of abdominal fat from water-fat MRI." Journal of Magnetic Resonance Imaging 37, no. 2 (September 25, 2012): 423–30. http://dx.doi.org/10.1002/jmri.23813.

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9

Nardo, Lorenzo, Dimitrios C. Karampinos, Drew A. Lansdown, Julio Carballido-Gamio, Sonia Lee, Roberto Maroldi, C. Benjamin Ma, Thomas M. Link, and Roland Krug. "Quantitative assessment of fat infiltration in the rotator cuff muscles using water-fat MRI." Journal of Magnetic Resonance Imaging 39, no. 5 (September 24, 2013): 1178–85. http://dx.doi.org/10.1002/jmri.24278.

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10

Gifford, Aliya, Joel Kullberg, Johan Berglund, Filip Malmberg, Katie C. Coate, Phillip E. Williams, Alan D. Cherrington, Malcolm J. Avison, and E. Brian Welch. "Canine body composition quantification using 3 tesla fat-water MRI." Journal of Magnetic Resonance Imaging 39, no. 2 (April 17, 2013): 485–91. http://dx.doi.org/10.1002/jmri.24156.

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11

Gifford, Aliya, Joel Kullberg, Johan Berglund, Filip Malmberg, Katie C. Coate, Phillip E. Williams, Alan D. Cherrington, Malcolm J. Avison, and E. Brian Welch. "Canine body composition quantification using 3 tesla fat-water MRI." Journal of Magnetic Resonance Imaging 39, no. 2 (February 2014): spcone. http://dx.doi.org/10.1002/jmri.24701.

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12

Wang, Hui-Chun, Po-Chou Chen, Chun-Hsiung Chou, Cherng-Gueih Shy, and Jo-Chi Jao. "COMPARISON OF VARIOUS MRI FAT SUPPRESSION TECHNIQUES ON A WATER-FAT PHANTOM AT 1.5 T." Biomedical Engineering: Applications, Basis and Communications 29, no. 02 (April 2017): 1750015. http://dx.doi.org/10.4015/s1016237217500156.

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Nowadays, magnetic resonance imaging (MRI) has been widely applied for diagnosis of soft-tissue diseases. Most clinical MRI protocols use fat suppression (FS) methods to suppress fat signal, reduce chemical shift artifacts, and increase conspicuity of lesions. To understand the advantages, disadvantages, and clinical applications of the most commonly used FS methods is an important issue. The aim of this study was to evaluate FS performance of six FS methods on a fat-water phantom at 1.5[Formula: see text]T. The six MRI methods included iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL), short inversion time inversion recovery (STIR), and four chemical presaturation (Chem Presat) methods. The phantom was composed of homogeneous oil-in-water emulsions with various fat contents ranging from 0 to 100% in increments of 10%. The difference between the suppressed fat fractions (FS fractions) and the true fat fractions of the phantom was used as an index of FS performance. The correlations and levels of agreement (LOAs) between the FS fractions determined using each FS method and the true fat fractions of the phantom were analyzed. From the phantom study, it was found that FSE T2 FS, STIR and IDEAL could achieve more accurate FS fractions than the other three methods. The FS fractions determined using FSE T2 FS, STIR and IDEAL were in a good agreement. On the contrary, T2-weighted spin echo Chem Presat had the most inaccurate quantification of FS fractions among these six FS methods. Both the ranks of signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of the phantom were IDEAL [Formula: see text] FSE T2 FS [Formula: see text] STIR. The FS performance of these six FS methods in clinical use needs further study.
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13

Chang, Jerry S., Bachir Taouli, Nouha Salibi, Elizabeth M. Hecht, Deanna G. Chin, and Vivian S. Lee. "Opposed-Phase MRI for Fat Quantification in Fat-Water Phantoms with 1H MR Spectroscopy to Resolve Ambiguity of Fat or Water Dominance." American Journal of Roentgenology 187, no. 1 (July 2006): W103—W106. http://dx.doi.org/10.2214/ajr.05.0695.

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14

de Vrijer, Barbra, Stephanie Giza, Craig Olmstead, Debbie Penava, Genevieve Eastabrook, Timothy Regnault, and Charles McKenzie. "O-OBS-MFM-MD-070 Imaging Fetal Subcutaneous Fat Development Using 3D Water-Fat MRI." Journal of Obstetrics and Gynaecology Canada 39, no. 5 (May 2017): 387. http://dx.doi.org/10.1016/j.jogc.2017.03.020.

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15

Soher, Brian J., Cory Wyatt, Scott B. Reeder, and James R. MacFall. "Noninvasive temperature mapping with MRI using chemical shift water-fat separation." Magnetic Resonance in Medicine 63, no. 5 (April 23, 2010): 1238–46. http://dx.doi.org/10.1002/mrm.22310.

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16

Romu, Thobias, Nils Dahlström, Olof Dahlqvist Leinhard, and Magnus Borga. "Robust water fat separated dual-echo MRI by phase-sensitive reconstruction." Magnetic Resonance in Medicine 78, no. 3 (October 24, 2016): 1208–16. http://dx.doi.org/10.1002/mrm.26488.

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17

Chen, Weitian, and Dimitrios C. Karampinos. "Chemical‐shift encoding–based water–fat separation with multifrequency fat spectrum modeling in spin‐lock MRI." Magnetic Resonance in Medicine 83, no. 5 (October 8, 2019): 1608–24. http://dx.doi.org/10.1002/mrm.28026.

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18

Braun, M., W. I. Jung, O. Lutz, and R. Oeschey. "Selective Non-Excitation of Water or Fat Protons in Magnetic Resonance Imaging." Zeitschrift für Naturforschung A 42, no. 12 (December 1, 1987): 1391–95. http://dx.doi.org/10.1515/zna-1987-1204.

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Nuclear magnetic resonance imaging (MRI) of water and fat protons has been performed with a 1.5 T whole body imager. The highly selective excitation, necessary for the discrimination of the two proton species, has been achieved by different four and five pulse excitation schemes which had to be adapted to the needs of MRI and completed to imaging sequences. Their ability to produce well separated water and fat distribution images of test objects is demonstrated. The special features of the method such as signal-to-noise ratio, insensitivity to rf-field inhomogeneities, ease of implementation and data handling are discussed and compared to existing spectral separation techniques.
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19

Farrelly, Cormac, Saurabh Shah, Amir Davarpanah, Aoife N. Keeling, and James C. Carr. "ECG-Gated Multiecho Dixon Fat-Water Separation in Cardiac MRI: Advantages Over Conventional Fat-Saturated Imaging." American Journal of Roentgenology 199, no. 1 (July 2012): W74—W83. http://dx.doi.org/10.2214/ajr.11.7759.

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20

Baum, Thomas, Samuel P. Yap, Michael Dieckmeyer, Stefan Ruschke, Holger Eggers, Hendrik Kooijman, Ernst J. Rummeny, Jan S. Bauer, and Dimitrios C. Karampinos. "Assessment of whole spine vertebral bone marrow fat using chemical shift-encoding based water-fat MRI." Journal of Magnetic Resonance Imaging 42, no. 4 (February 2, 2015): 1018–23. http://dx.doi.org/10.1002/jmri.24854.

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21

Ruschke, Stefan, Amber Pokorney, Thomas Baum, Holger Eggers, Jeffrey H. Miller, Houchun H. Hu, and Dimitrios C. Karampinos. "Measurement of vertebral bone marrow proton density fat fraction in children using quantitative water–fat MRI." Magnetic Resonance Materials in Physics, Biology and Medicine 30, no. 5 (April 5, 2017): 449–60. http://dx.doi.org/10.1007/s10334-017-0617-0.

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22

TSUBAHARA, Akio, Naoichi CHINO, Kunitsugu KONDOH, and Yasutomo OKAJIMA. "Hemiplegic Muscular Atrophy Evaluated by Fat/Water Suppression Magnetic Resonance Imaging(MRI)." Japanese Journal of Rehabilitation Medicine 33, no. 10 (1996): 701–9. http://dx.doi.org/10.2490/jjrm1963.33.701.

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23

Hu, Houchun H., Thomas G. Perkins, Jonathan M. Chia, and Vicente Gilsanz. "Characterization of Human Brown Adipose Tissue by Chemical-Shift Water-Fat MRI." American Journal of Roentgenology 200, no. 1 (January 2013): 177–83. http://dx.doi.org/10.2214/ajr.12.8996.

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24

Rasmussen, Jerod M., Sonja Entringer, Annie Nguyen, Theo G. M. van Erp, Ana Guijarro, Fariba Oveisi, James M. Swanson, et al. "Brown Adipose Tissue Quantification in Human Neonates Using Water-Fat Separated MRI." PLoS ONE 8, no. 10 (October 30, 2013): e77907. http://dx.doi.org/10.1371/journal.pone.0077907.

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25

Ong, Henry H., Corey D. Webb, Marnie L. Gruen, Alyssa H. Hasty, John C. Gore, and E. Brian Welch. "Fat-water MRI of a diet-induced obesity mouse model at 15.2T." Journal of Medical Imaging 3, no. 2 (May 24, 2016): 026002. http://dx.doi.org/10.1117/1.jmi.3.2.026002.

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26

de Vrijer, B., S. Giza, C. Olmstead, D. Penava, G. Eastabrook, T. R. H. Regnault, and C. A. McKenzie. "Insight Inside: Imaging fetal adipose tissue development with 3D water-fat MRI." Placenta 51 (March 2017): 108. http://dx.doi.org/10.1016/j.placenta.2017.01.040.

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27

Hu, Houchun H., Daniel L. Smith, Krishna S. Nayak, Michael I. Goran, and Tim R. Nagy. "Identification of brown adipose tissue in mice with fat-water IDEAL-MRI." Journal of Magnetic Resonance Imaging 31, no. 5 (May 2010): 1195–202. http://dx.doi.org/10.1002/jmri.22162.

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28

Kullberg, Joel, Ann-Katrine Karlsson, Eira Stokland, Pär-Arne Svensson, and Jovanna Dahlgren. "Adipose tissue distribution in children: Automated quantification using water and fat MRI." Journal of Magnetic Resonance Imaging 32, no. 1 (May 26, 2010): 204–10. http://dx.doi.org/10.1002/jmri.22193.

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29

Ho, Kai-Yu, Houchun H. Hu, Joyce H. Keyak, Patrick M. Colletti, and Christopher M. Powers. "Measuring bone mineral density with fat-water MRI: comparison with computed tomography." Journal of Magnetic Resonance Imaging 37, no. 1 (July 10, 2012): 237–42. http://dx.doi.org/10.1002/jmri.23749.

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30

Hu, Houchun Harry, Peter Börnert, Diego Hernando, Peter Kellman, Jingfei Ma, Scott Reeder, and Claude Sirlin. "ISMRM workshop on fat-water separation: Insights, applications and progress in MRI." Magnetic Resonance in Medicine 68, no. 2 (June 12, 2012): 378–88. http://dx.doi.org/10.1002/mrm.24369.

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31

Wu, Yaotang, Guangping Dai, Jerome L. Ackerman, Mirko I. Hrovat, Melvin J. Glimcher, Brian D. Snyder, Ara Nazarian, and David A. Chesler. "Water- and fat-suppressed proton projection MRI (WASPI) of rat femur bone." Magnetic Resonance in Medicine 57, no. 3 (2007): 554–67. http://dx.doi.org/10.1002/mrm.21174.

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32

Schär, Michael, Holger Eggers, Nicholas R. Zwart, Yuchou Chang, Akshay Bakhru, and James G. Pipe. "Dixon water-fat separation in PROPELLER MRI acquired with two interleaved echoes." Magnetic Resonance in Medicine 75, no. 2 (March 13, 2015): 718–28. http://dx.doi.org/10.1002/mrm.25656.

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33

Fangping Huang, S. Narayan, D. Wilson, D. Johnson, and Guo-Qiang Zhang. "A Fast Iterated Conditional Modes Algorithm for Water–Fat Decomposition in MRI." IEEE Transactions on Medical Imaging 30, no. 8 (August 2011): 1480–92. http://dx.doi.org/10.1109/tmi.2011.2125980.

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34

Lin, Ming-Fang, Lu-Han Lai, Wen-Tien Hsiao, Melissa Min-Szu Yao, and Wing-P. Chan. "Developing a Specific MRI Technology to Identify Complications Caused by Breast Implants." Applied Sciences 11, no. 8 (April 12, 2021): 3434. http://dx.doi.org/10.3390/app11083434.

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With advancements in aesthetic medicine, breast augmentation has become a popular plastic surgery worldwide, typically performed using either fine-needle injection or silicone implants. Both carry complication risks from rupture over time. In this study, we aimed to reduce misjudgments and increase diagnostic value by developing an MRI technique that can produce water- and silicone-specific images from MRI scans of phantoms (Natrelle® saline-filled breast implants) and human bodies. Pig oil, soybean oil, and normal saline were used to simulate human breast tissue, and two common types of breast implants, saline bags, and silicone bags, were selected as well, resulting in five materials scanned. Six pulse sequences were applied: T1W fast spin echo (FSE), T1W SPGR/60, T2W, T2W fat-saturation, STIR, and STIR water-saturation. Human body scans were additionally investigated using 3D SPGR fat-saturation dynamic contrast enhancement. Results show that the best way to enhance tissue contrast in images of silicone implants is to apply STIR combined with water suppression, and the best way to enhance saline bag implants is to apply T2W fat-saturation combined with fat suppression. Both offered very high sensitivity and specificity, rendering this method especially useful for distinguishing normal mammary glands from siliconoma.
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35

Dieckmeyer, Michael, Stephanie Inhuber, Sarah Schläger, Dominik Weidlich, Muthu R. K. Mookiah, Karupppasamy Subburaj, Egon Burian, et al. "Association of Thigh Muscle Strength with Texture Features Based on Proton Density Fat Fraction Maps Derived from Chemical Shift Encoding-Based Water–Fat MRI." Diagnostics 11, no. 2 (February 13, 2021): 302. http://dx.doi.org/10.3390/diagnostics11020302.

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Purpose: Based on conventional and quantitative magnetic resonance imaging (MRI), texture analysis (TA) has shown encouraging results as a biomarker for tissue structure. Chemical shift encoding-based water–fat MRI (CSE-MRI)-derived proton density fat fraction (PDFF) of thigh muscles has been associated with musculoskeletal, metabolic, and neuromuscular disorders and was demonstrated to predict muscle strength. The purpose of this study was to investigate PDFF-based TA of thigh muscles as a predictor of thigh muscle strength in comparison to mean PDFF. Methods: 30 healthy subjects (age = 30 ± 6 years; 15 females) underwent CSE-MRI of the lumbar spine at 3T, using a six-echo 3D spoiled gradient echo sequence. Quadriceps (EXT) and ischiocrural (FLEX) muscles were segmented to extract mean PDFF and texture features. Muscle flexion and extension strength were measured with an isokinetic dynamometer. Results: Of the eleven extracted texture features, Variance(global) showed the highest significant correlation with extension strength (p < 0.001, R2adj = 0.712), and Correlation showed the highest significant correlation with flexion strength (p = 0.016, R2adj = 0.658). Multivariate linear regression models identified Variance(global) and sex, but not PDFF, as significant predictors of extension strength (R2adj = 0.709; p < 0.001), while mean PDFF, sex, and BMI, but none of the texture features, were identified as significant predictors of flexion strength (R2adj = 0.674; p < 0.001). Conclusions: Prediction of quadriceps muscle strength can be improved beyond mean PDFF by means of TA, indicating the capability to quantify muscular fat infiltration patterns.
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36

Güttsches, Anne-Katrin, Robert Rehmann, Anja Schreiner, Marlena Rohm, Johannes Forsting, Martijn Froeling, Martin Tegenthoff, Matthias Vorgerd, and Lara Schlaffke. "Quantitative Muscle-MRI Correlates with Histopathology in Skeletal Muscle Biopsies." Journal of Neuromuscular Diseases 8, no. 4 (July 30, 2021): 669–78. http://dx.doi.org/10.3233/jnd-210641.

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Background: Skeletal muscle biopsy is one of the gold standards in the diagnostic workup of muscle disorders. By histopathologic analysis, characteristic features like inflammatory cellular infiltrations, fat and collagen replacement of muscle tissue or structural defects of the myofibers can be detected. In the past years, novel quantitative MRI (qMRI) techniques have been developed to quantify tissue parameters, thus providing a non-invasive diagnostic tool in several myopathies. Objective: This proof-of-principle study was performed to validate the qMRI-techniques to skeletal muscle biopsy results. Methods: Ten patients who underwent skeletal muscle biopsy for diagnostic purposes were examined by qMRI. Fat fraction, water T2-time and diffusion parameters were measured in the muscle from which the biopsy was taken. The proportion of fat tissue, the severity of degenerative and inflammatory parameters and the amount of type 1- and type 2- muscle fibers were determined in all biopsy samples. The qMRI-data were then correlated to the histopathological findings. Results: The amount of fat tissue in skeletal muscle biopsy correlated significantly with the fat fraction derived from the Dixon sequence. The water T2-time, a parameter for tissue edema, correlated with the amount of vacuolar changes of myofibers and endomysial macrophages in the histopathologic analysis. No significant correlations were found for diffusion parameters. Conclusion: In this proof-of-principle study, qMRI techniques were related to characteristic histopathologic features in neuromuscular disorders. The study provides the basis for further development of qMRI methods in the follow-up of patients with neuromuscular disorders, especially in the context of emerging treatment strategies.
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37

McNeill, G., P. A. Fowler, R. J. Maughan, B. A. McGaw, M. F. Fuller, D. Gvozdanovic, and S. Gvozdanovic. "Body fat in lean and overweight women estimated by six methods." British Journal of Nutrition 65, no. 2 (March 1991): 95–103. http://dx.doi.org/10.1079/bjn19910072.

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Body fat content of seven lean women (body mass index (BMI) 20.6 (sd1.8) kg/m2) and seven overweight women (BMI 31.1 (sd 3.3) kg/m2) was estimated by six different methods: underwater weighing (UWW), body-water dilution (BWD), whole-body counting (40K), skinfold thickness (SFT), bio-electrical impedance (BEI) and magnetic resonance imaging (MRI). Using UWW as the reference method, the differences between percentage fat by each other method and the percentage fat by UWW were calculated for each subject. The mean difference was lowest for SFT and highest for BWD. MRI showed the lowest variability in individual results, and 40K the highest. 40K and BWD methods used in combination gave better agreement with UWW results than either 40K or BWD methods alone. There was a weak negative correlation between the difference from the UWW results and percentage fat in the SFT measurements, but not in the BWD, 40K, BEI or MRI measurements, suggesting that for these methods the assumptions involved produced no greater inaccuracy in the overweight women than in the lean women. In all subjects the BEI offered little improvement over the traditional SFT measurements. The agreement between MRI and UWW estimates in both lean and overweight women suggests that MRI may be a satisfactory substitute for the more established methods of body fat estimation in adult women.
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38

Havla, Lukas, Tamer Basha, Hussein Rayatzadeh, Jaime L. Shaw, Warren J. Manning, Scott B. Reeder, Sebastian Kozerke, and Reza Nezafat. "Improved fat water separation with water selective inversion pulse for inversion recovery imaging in cardiac MRI." Journal of Magnetic Resonance Imaging 37, no. 2 (August 23, 2012): 484–90. http://dx.doi.org/10.1002/jmri.23779.

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39

Chen, Chiao‐Chi, Yi‐Jui Liu, Shiou‐Ping Lee, Hou‐Ting Yang, and Wing P. Chan. "Gender interactions between vertebral bone mineral density and fat content in the elderly: Assessment using fat–water MRI." Journal of Magnetic Resonance Imaging 51, no. 5 (October 18, 2019): 1382–89. http://dx.doi.org/10.1002/jmri.26956.

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40

Huang, Jianpan, Lin Chen, Kannie W. Y. Chan, Congbo Cai, Shuhui Cai, and Zhong Chen. "Super-resolved water/fat image reconstruction based on single-shot spatiotemporally encoded MRI." Journal of Magnetic Resonance 314 (May 2020): 106736. http://dx.doi.org/10.1016/j.jmr.2020.106736.

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41

Schlaeger, Sarah, Stephanie Inhuber, Alexander Rohrmeier, Michael Dieckmeyer, Friedemann Freitag, Elisabeth Klupp, Dominik Weidlich, et al. "Association of paraspinal muscle water–fat MRI-based measurements with isometric strength measurements." European Radiology 29, no. 2 (July 16, 2018): 599–608. http://dx.doi.org/10.1007/s00330-018-5631-8.

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42

Rahimi, Mahdi Salmani, James H. Holmes, Kang Wang, Scott B. Reeder, and Frank R. Korosec. "Flow-induced signal misallocation artifacts in two-point fat-water chemical shift MRI." Magnetic Resonance in Medicine 73, no. 5 (June 9, 2014): 1926–31. http://dx.doi.org/10.1002/mrm.25315.

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43

Son, Jong Bum, Steven M. Wright, and Jim X. Ji. "Single‐point Dixon water‐fat imaging using 64‐channel single‐echo acquisition MRI." Concepts in Magnetic Resonance Part B: Magnetic Resonance Engineering 33B, no. 3 (August 2008): 152–62. http://dx.doi.org/10.1002/cmr.b.20120.

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44

Koreman, Tianna, Stephanie A. Giza, Genevieve Eastabrook, Debbie Penava, Charles A. McKenzie, and Barbra de Vrijer. "Fetal Subcutaneous Fat by 3D Water-Fat MRI is Independent of Maternal Obesity, Excessive Pregnancy Weight Gain and Diabetes." Journal of Obstetrics and Gynaecology Canada 40, no. 6 (June 2018): 839. http://dx.doi.org/10.1016/j.jogc.2018.03.067.

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45

Hines, Catherine D. G., Huanzhou Yu, Ann Shimakawa, Charles A. McKenzie, Jean H. Brittain, and Scott B. Reeder. "T1independent, T2* corrected MRI with accurate spectral modeling for quantification of fat: Validation in a fat-water-SPIO phantom." Journal of Magnetic Resonance Imaging 30, no. 5 (November 2009): 1215–22. http://dx.doi.org/10.1002/jmri.21957.

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46

Franssens, Bas T., Anouk L. Eikendal, Tim Leiner, Yolanda van der Graaf, Frank L. J. Visseren, and J. M. Hoogduin. "Reliability and agreement of adipose tissue fat fraction measurements with water-fat MRI in patients with manifest cardiovascular disease." NMR in Biomedicine 29, no. 1 (December 1, 2015): 48–56. http://dx.doi.org/10.1002/nbm.3444.

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47

Li, Guanwu, Zheng Xu, Hao Gu, Xuefeng Li, Wei Yuan, Shixin Chang, Jingzheng Fan, Horea Calimente, and Jiani Hu. "Comparison of chemical shift-encoded water-fat MRI and MR spectroscopy in quantification of marrow fat in postmenopausal females." Journal of Magnetic Resonance Imaging 45, no. 1 (June 24, 2016): 66–73. http://dx.doi.org/10.1002/jmri.25351.

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48

Giza, Stephanie A., Michael R. Miller, Prasiddha Parthasarathy, Barbra de Vrijer, and Charles A. McKenzie. "Comparison of modified two-point dixon and chemical shift encoded MRI water-fat separation methods for fetal fat quantification." Journal of Magnetic Resonance Imaging 48, no. 1 (January 10, 2018): 274–82. http://dx.doi.org/10.1002/jmri.25929.

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49

Ji, Yayun, Weifeng Hong, Mouyuan Liu, Yuying Liang, YongYan Deng, and Liheng Ma. "Intervertebral disc degeneration associated with vertebral marrow fat, assessed using quantitative magnetic resonance imaging." Skeletal Radiology 49, no. 11 (May 28, 2020): 1753–63. http://dx.doi.org/10.1007/s00256-020-03419-7.

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Abstract Objective To investigate the potential clinical application of quantitative MRI in assessing the correlation between lumbar vertebrae bone marrow fat deposition and intervertebral disc degeneration. Materials and methods A total of 104 chronic lower-back pain volunteers underwent 3.0-T MRI with T2-weighted imaging, T2 mapping, and iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL-IQ) between August 2018 and June 2019. Each disc was assessed with T2 value by T2 mapping, and the L1-S1 vertebral bone marrow fat fraction was assessed by IDEAL-IQ. The differences and relationship between T2 value and the adjacent vertebral bone marrow fat fraction values within the five Pfirrmann groups, five age groups, and five lumbar levels were statistically analyzed. Results The vertebral bone marrow fat fraction had a significant negative correlation with T2 values of nucleus pulposus’ T2 values (p < 0.001). However, the significant negative correlation was only found between T2 values of nucleus pulposus and adjacent vertebral bone marrow fat in Pfirrmann II–III, L1/2-L5/S1 level, and 40–49 years’ age groups. Pfirrmann grades of the intervertebral disc were positively correlated with adjacent vertebrae bone marrow fat fraction (p < 0.05). Conclusion Lumbar bone marrow fat deposition significantly increases during the early stages of intervertebral disc degeneration. Quantitative measurements of bone marrow fat deposition and water content of intervertebral discs have a predictive value and are an important supplement to the qualitative traditional classification strategies for the early stages of intervertebral disc degeneration.
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50

Burian, Egon, Jan Syväri, Christina Holzapfel, Theresa Drabsch, Jan Kirschke, Ernst Rummeny, Claus Zimmer, et al. "Gender- and Age-Related Changes in Trunk Muscle Composition Using Chemical Shift Encoding-Based Water–Fat MRI." Nutrients 10, no. 12 (December 13, 2018): 1972. http://dx.doi.org/10.3390/nu10121972.

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Ageing, sarcopenia, and malnutrition are associated with quantitative and qualitative changes of body composition. There are several imaging modalities, including magnetic resonance imaging (MRI), for the assessment of trunk muscle tissue composition. In this study, we investigated the gender- and age-related changes in trunk muscle composition using chemical shift encoding-based water–fat MRI. A total of 79 healthy volunteers (26 men: 38.9 ± 10.4 years; 53 women: 39.5 ± 15.0 years) underwent 3T axial MRI using a six-echo multi-echo 3D spoiled gradient echo sequence, allowing for the calculation of the proton density fat fraction (PDFF) in the trunk muscles. PDFF of the abdominal, psoas, and erector spinae muscles were determined. We detected significant positive correlations for abdominal muscle PDFF with age (r = 0.638, p = 0.0001) in men, and for abdominal muscle PDFF (r = 0.709, p = 0.0001) and erector spinae muscle PDFF (r = 0.674, p = 0.0001) with age in women. After adjustment for body mass index (BMI), only the correlation of age and abdominal muscle PDFF in women remained significant (r = 0.631, p = 0.0001). The findings of this study suggest that an increasing fat deposition in muscle is driven primarily by age, rather than BMI, in women. These results further support that PDFF can be considered a valid imaging biomarker of trunk muscle composition.
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