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Usefulness of fat-containing agents: an initial study on estimating fat content for magnetic resonance imaging

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Abstract

This initial study aimed at testing whether fat-containing agents can be used for the fat mass estimation methods using magnetic resonance imaging (MRI). As an example for clinical application, fat-containing agents (based on soybean oil, 10% and 20%), 100% soybean oil, and saline as reference substances were placed outside the proximal femurs obtained from 14 participants and analyzed by 0.3 T MRI. Fat content was the estimated fat fraction (FF) based on signal intensity (SIeFF, %). The SIeFF values of the femoral bone marrow, including the femoral head, neck, shaft, and trochanter area, were measured. MRI data were compared in terms of bone mineral content (BMC) and bone mineral density (BMD) by dual-energy X-ray absorptiometry (DXA) in the proximal femur. Twelve pig femurs were also used to confirm the correlation between FF by the DIXON method and SIeFF. According to Pearson’s correlation coefficient, the SIeFF and total BMC and BMD data revealed strong and moderate negative correlations in the femoral head (r <  − 0.74) and other sites (r =  − 0.66 to − 0.45). FF and SIeFF showed a strong correlation (r = 0.96). This study was an initial investigation of a method for estimating fat mass with fat-containing agents and showed the potential for use in MRI. SIeFF and FF showed a strong correlation, and SIeFF and BMD and BMC showed correlation; however, further studies are needed to use SIeFF as a substitute for DXA.

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References

  1. Reeder SB, Cruite I, Hamilton G, Sirlin CB (2011) Quantitative assessment of liver fat with magnetic resonance imaging and spectroscopy. J Magn Reson Imaging 34:729–749. https://doi.org/10.1002/jmri.22775

    Article  PubMed  PubMed Central  Google Scholar 

  2. Gaeta M, Messina S, Mileto A, Vita GL, Ascenti G, Vinci S, Bottari A, Vita G, Settineri N, Bruschetta D, Racchiusa S, Minutoli F (2012) Muscle fat-fraction and mapping in Duchenne muscular dystrophy: evaluation of disease distribution and correlation with clinical assessments. Preliminary experience. Skelet Radiol 41:955–961. https://doi.org/10.1007/s00256-011-1301-5

    Article  Google Scholar 

  3. Gondim Teixeira PA, Cherubin T, Badr S, Bedri A, Gillet R, Albuisson E, Blum A (2019) Proximal femur fat fraction variation in healthy subjects using chemical shift-encoding based MRI. Sci Rep 9:20212. https://doi.org/10.1038/s41598-019-56611-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Cordes C, Baum T, Dieckmeyer M, Ruschke S, Diefenbach MN, Hauner H, Kirschke JS, Karampinos DC (2016) MR-based assessment of bone marrow fat in osteoporosis, diabetes, and obesity. Front Endocrinol (Lausanne) 7:74. https://doi.org/10.3389/fendo.2016.00074

    Article  PubMed  Google Scholar 

  5. Paccou J, Penel G, Chauveau C, Cortet B, Hardouin P (2019) Marrow adiposity and bone: review of clinical implications. Bone 118:8–15. https://doi.org/10.1016/j.bone.2018.02.008

    Article  PubMed  Google Scholar 

  6. Zhang L, Li S, Hao S, Yuan Z (2016) Quantification of fat deposition in bone marrow in the lumbar vertebra by proton MRS and in-phase and out-of-phase MRI for the diagnosis of osteoporosis. J Xray Sci Technol 24:257–266. https://doi.org/10.3233/XST-160549

    Article  CAS  PubMed  Google Scholar 

  7. Cordes C, Baum T, Dieckmeyer M, Ruschke S, Diefenback MN, Hauner H, Kirschke JS, Karampinos DC (2016) MR-based assessment of bone marrow fat in osteoporosis, diabetes, and obesity. Front Endocrinol (Lausanne) 7:74. https://doi.org/10.3389/fendo.2016.00074

    Article  PubMed  Google Scholar 

  8. Dixon WT (1984) Simple proton spectroscopic imaging. Radiology 153:189–194. https://doi.org/10.1148/radiology.153.1.6089263

    Article  CAS  PubMed  Google Scholar 

  9. Zhang Y, Zhou Z, Wang C, Cheng X, Wang L, Duanmu Y, Zhang C, Veronese N, Guglielmi G (2018) Reliability of measuring the fat content of the lumbar vertebral marrow and paraspinal muscles using MRI mDIXON-Quant sequence. Diagn Interv Radiol 24:302–307. https://doi.org/10.5152/dir.2018.17323

    Article  PubMed  PubMed Central  Google Scholar 

  10. Loau J, Shiehmorteza M, Girard OM, Sirlin CB, Bydder M (2013) Evaluation of MRI fat fraction in the liver and spine pre and post SPIO infusion. Magn Reson Imaging 31:1012–1016. https://doi.org/10.1016/j.mri.2013.01.016

    Article  Google Scholar 

  11. Fishbein MH, Gardner KG, Potter CJ, Schmalbrock P, Smith MA (1997) Introduction of fast MR imaging in the assessment of hepatic steatosis. Magn Reson Imaging 15:287–293. https://doi.org/10.1016/s0730-725x(96)00224-x

    Article  CAS  PubMed  Google Scholar 

  12. Sane S, Baba M, Kusano C, Shirao K, Kamada T, Aikou T (1999) Fat emulsion administration in the early postoperative period in patients undergoing esophagectomy for carcinoma depresses arachidonic acid metabolism in neutrophils. Nutrition 15:341–346. https://doi.org/10.1016/s0899-9007(99)00032-5

    Article  CAS  PubMed  Google Scholar 

  13. Hoshino R, Kamiya Y, Fujii Y, Ttsubokawa T (2017) Lipid emulsion injection-induced reversal of cardiac toxicity and acceleration of emergence from general anesthesia after scalp infiltration of a local anesthetic: a case report. JA Clin Rep 3:9. https://doi.org/10.1186/s40981-017-0077-6

    Article  PubMed  PubMed Central  Google Scholar 

  14. Peterson P, Svensson J, Månsson S (2014) Relaxation effects in MRI-based quantification of fat content and fatty acid composition. Magn Reason Med 72:1320–1329. https://doi.org/10.1002/mrm.25048

    Article  CAS  Google Scholar 

  15. Mashhood A, Railkar R, Yokoo T, Levin Y, Clark L, Fox-Bosetti S, Middleton MS, Riek J, Kauh E, Dardzinski BJ, Williams D, Sirlin C, Shire NJ (2013) Reproducibility of hepatic fat fraction measurement by magnetic resonance imaging. J Magn Reason Imaging 37:1359–1370. https://doi.org/10.1002/jmri.23928

    Article  Google Scholar 

  16. Paccou J, Hardouin P, Cotten A, Penel G, Cortet B (2015) The role of bone marrow fat in skeletal health: usefulness and perspectives for clinicians. J Clin Endocrinol Metab 100:3613–3621. https://doi.org/10.1210/jc.2015-2338

    Article  CAS  PubMed  Google Scholar 

  17. Baum T, Yap SP, Karampinos DC, Nardo L, Kuo D, Burghardt AJ, Masharani UB, Schwartz AV, Li X, Link TM (2012) Does vertebral bone marrow fat content correlate with abdominal adipose tissue, lumbar spine BMD and blood biomarkers in women with type 2 diabetes mellitus? J Magn Reson Imaging 35:117–124. https://doi.org/10.1002/jmri.22757

    Article  PubMed  Google Scholar 

  18. Cummings SR, Black D (1995) Bone mass measurements and risk of fracture in Caucasian women: a review of findings from prospective studies. Am J Med 98:24S-28S. https://doi.org/10.1016/s0002-9343(05)80041-5

    Article  CAS  PubMed  Google Scholar 

  19. Pietschmann P, Rauner M, Sipos W, Kerschan-Schindl K (2009) Osteoporosis: an age-related and gender-specific disease–a mini-review. Gerontology 55:3–12. https://doi.org/10.1159/000166209

    Article  PubMed  Google Scholar 

  20. Hofbauer LC, Brueck CC, Singh SK, Dobnig H (2007) Review osteoporosis in patients with Diabetes Mellitus. J Bone Miner Res 22:1317–1328. https://doi.org/10.1359/jbmr.070510

    Article  CAS  PubMed  Google Scholar 

  21. Zhao LJ, Jiang H, Papasian CJ, Maulik D, Dress B, Hamilton J, Deng HW (2008) Review correlation of obesity and osteoporosis: effect of fat mass on the determination of osteoporosis. J Bone Miner Res 23:17–29. https://doi.org/10.1359/jbmr.070813

    Article  CAS  PubMed  Google Scholar 

  22. WHO study group (1994) Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. WHO technical report series 843, Geneva, Switzland.

  23. Bredella MA, Daley SM, Kalra MK, Keenan BJ, Miller KK, Torriani M (2015) Marrow adipose tissue quantification of the lumbar spine by using dual-energy CT and single-voxel (1)H MR spectroscopy: a feasibility study. Radiology 277:230–235. https://doi.org/10.1148/radiol.2015142876

    Article  PubMed  Google Scholar 

  24. Japan Osteoporosis Society, Bone Strength Evaluation Committee (2007) Proximal femur BMD measurement manual (in Japanese). Osteoporos Jpn 15:1–41

    Google Scholar 

  25. Belaroussi B, Milles J, Carme S, Zhu YM, Benoit-Cattin H (2006) Intensity non-uniformity correction in MRI: existing methods and their validation. Med Image Anal 10:234–246. https://doi.org/10.1016/j.media.2005.09.004

    Article  PubMed  Google Scholar 

  26. Sieron D, Drakopoulos D, Loebelenz LI, Schroeder C, Ebner L, Obmann VC, Huber AT, Christe A (2020) Correlation between fat signal ratio on T1-weighted MRI in the lower vertebral bodies and age, comparing 1.5-T and 3-T scanners. Acta Radiol Open 9:2058460120901517. https://doi.org/10.1177/2058460120901517

    Article  PubMed  PubMed Central  Google Scholar 

  27. Meurens F, Summerfield A, Nauwynck H, Saif L, Gerdts V (2012) The pig: a model for human infectious diseases. Trends Microbiol 20:50–57. https://doi.org/10.1016/j.tim.2011.11.002

    Article  CAS  PubMed  Google Scholar 

  28. Reichert JC, Saifzadeh S, Wullschleger ME, Epari DR, Schütz MA, Duda GN, Schell H, van Griensven M, Redl H, Hutmacher DW (2009) The challenge of establishing preclinical models for segmental bone defect research. Biomaterials 30:2149–2163. https://doi.org/10.1016/j.biomaterials.2008.12.050

    Article  CAS  PubMed  Google Scholar 

  29. Aerssens J, Boonen S, Lowet G, Dequeker J (1998) Interspecies differences in bone composition, density, and quality: potential implications for in vivo bone research. Endocrinology 139:663–670. https://doi.org/10.1210/endo.139.2.5751

    Article  CAS  PubMed  Google Scholar 

  30. Thorwarth M, Schultze-Mosgau S, Kessler P, Wiltfang J, Schlegel KA (2005) Bone regeneration in osseous defects using a resorbable nanoparticular hydroxyapatite. J Oral Maxillofac Surg 63:1626–1633. https://doi.org/10.1016/j.joms.2005.06.010

    Article  PubMed  Google Scholar 

  31. Kanda Y (2013) Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant 48:452–458. https://doi.org/10.1038/bmt.2012.244

    Article  CAS  PubMed  Google Scholar 

  32. Binkovitz LA, Henwood MJ (2007) Pediatric DXA: technique and interpretation. Pediatr Radiol 37:21–31. https://doi.org/10.1007/s00247-006-0153-y

    Article  PubMed  Google Scholar 

  33. Carter DR, Bouxsein ML, Marcus R (1992) New approaches for interpreting projected bone densitometry data. J Bone Miner Res 7:137–145. https://doi.org/10.1002/jbmr.5650070204

    Article  CAS  PubMed  Google Scholar 

  34. Prentice A, Parsons TJ, Cole TJ (1994) Uncritical use of bone mineral density in absorptiometry may lead to size- related artifacts in the identification of bone mineral determinants. Am J Clin Nutr 60:837–842. https://doi.org/10.1093/ajcn/60.6.837

    Article  CAS  PubMed  Google Scholar 

  35. Kröger H, Vainio P, Nieminen J, Kotaniemi A (1995) Comparison of different models for interpreting bone mineral density measurements using DXA and MRI technology. Bone 17:157–159. https://doi.org/10.1016/s8756-3282(95)00162-x

    Article  PubMed  Google Scholar 

  36. Arokoski MH, Arokoski JPA, Vainio P, Niemitukia LH, Kröger H, Jurvelin JS (2002) Comparison of DXA and MRI methods for interpreting femoral neck bone mineral density. J Clin Densitom 5:289–296. https://doi.org/10.1385/jcd:5:3:289

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors would like to thank Mr. Koji Uchida in Center for Information and Neural Networks National Institute of Information and Communications Technology, Dr. Shunichi Motegi in Gunma Paz University, Dr. Rei Yoshida in Kurihara Central Hospital, Mis. Yuriko Nohara in Gazou no mori Diagnostic Clinic, Kenichiro Yamamura in Tokushima Bunri University, and Kunihiro Yabe in Yamagata Prefectural Shinjo Hospital for his valuable advice and technical support on measurements.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Molecular Imaging, School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-Cho, Toyoake, Aichi, 470-1192, Japan

    Yasuo Takatsu

  2. Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, 920-0942, Japan

    Yasuo Takatsu & Tosiaki Miyati

  3. Department of Radiology, Geisei Ortho Clinic, 1495-1, Wajikikou, Geisei-Mura, Aki-Gun, Kochi, 781-5701, Japan

    Hiroshi Ohnishi

  4. Department of Intelligent Information Engineering, School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-Cho, Toyoake, Aichi, 470-1192, Japan

    Tomoko Tateyama

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Correspondence to Yasuo Takatsu.

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Takatsu, Y., Ohnishi, H., Tateyama, T. et al. Usefulness of fat-containing agents: an initial study on estimating fat content for magnetic resonance imaging. Phys Eng Sci Med 47, 339–350 (2024). https://doi.org/10.1007/s13246-023-01372-y

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