Logo Université Laval Logo Université Laval
International Chair on Cardiometabolic Risk
My Healthy Waist

Magnetic Resonance Imaging (MRI)

Evaluating CMR - Imaging Techniques

Key Points

  • MRI is a criterion method for assessing visceral fat.
  • MRI can be used to measure ectopic fat deposition in the liver and skeletal muscle.
  • High cost and limited availability currently hinder the routine use of this tool for assessing body composition and related health risk in clinical practice.

Imaging Techniques

There are several imaging techniques for determining total and regional body composition. Magnetic resonance imaging (MRI) provides cross-sectional images that can be used to determine total adiposity and are one of the most accurate tools available for quantifying body composition at a tissue level. Along with Computed Tomography (CT), MRI is often considered the criterion measure for assessing visceral fat and skeletal muscle in vivo. Other imaging techniques such as dual energy x-ray absorptiometry (DEXA) and ultrasound are also useful clinical techniques for assessing total and visceral adiposity. The strengths and weaknesses of DEXA and ultrasonography are addressed in the Other Imaging Techniques section.

Magnetic Resonance Imaging: How it Works

MRI uses the interaction between strong magnetic fields and hydrogen nuclei (protons)—which are abundant in all biological tissues—to create cross-sectional images of the body [1,2]. Unlike CT, MRI is not known to have any adverse side effects and is therefore the preferred method for assessing whole body tissue composition. However, MRI takes much longer to acquire images, and analyzing these images is a more complex and time-consuming process. Using multiple images acquired with standard clinical magnets (e.g., 1.5 or 3.0 Tesla), whole body MRI data for fat and lean mass can be acquired in about 45 minutes (Figure 1) [3-5].

Figure 1:
Whole body magnetic resonance imaging (MRI) acquisition

Determining Tissue Area or Mass

MRI images are normally analyzed using one of two methods: 1) the perimeter of the tissue of interest is traced manually [6,7], and the area within the perimeter is calculated by multiplying the number of pixels in the region of interest by their known area, or 2) image segmentation algorithms are used to identify all pixels within a selected range of intensities believed to be representative of a specific tissue. However, the latter approach is considered more problematic when applied to MRI images for three reasons: 1) distributions of pixel intensity (greyscale) values for different tissues overlap more for MRI than for CT images, 2) noise due to respiratory motion blurs the borders between tissues in the abdomen to a greater extent in MRI than in CT, and 3) inhomogeneity in the magnetic field can produce shading at the peripheries of MRI images [6].

With multiple MRI images, tissue volumes can be calculated by integrating cross-sectional area data from consecutive images. Because of the cost of image acquisition and analysis, images are typically collected with gaps between images (usually ranging from 20 to 40 mm), and volumes are then calculated using various modeling equations [3,4,8,9]. Tissue densities for adipose tissue, skeletal muscle, and organs are fairly constant from person to person, and volume measures for these tissues can be converted to mass units by multiplying the volume by assumed tissue density values [10,11].

Measuring Skeletal Muscle Mass

MRI and CT are the gold standard measures for in vivo quantification of skeletal muscle mass (Figure 2). Muscle mass and changes to it are related to muscle strength [12-14], and accurately determining skeletal muscle mass is particularly important in elderly populations who are at increased risk of sarcopenia and functional impairment due to low muscle mass. Measures of skeletal muscle by a single MRI image have been validated using cadaver measures and show a high level of agreement (R2=0.94, standard error of estimate=10%) and a low coefficient of variation (CV~2%) [15]. Compared to cadaver values, MRI error improved to approximately 1% when volume measures were acquired using multiple images. However, as this is a very time-consuming and expensive process, a single image at the mid-thigh is commonly used as a proxy measure of whole body skeletal muscle in both men and women (R2=0.77-0.79) [16].

Figure 2:
Greyscale non-segmented and segmented magnetic resonance imaging (MRI) image at the mid-thigh in an obese woman

Measuring Visceral Fat

MRI and CT are the only in vivo methods available to directly and accurately quantify visceral fat. Visceral fat is the fat that is located within the abdominal muscle wall and that surrounds the organs (or viscera). On average, it accounts for only 12% and 5% of total body fat content in men and women respectively (See Figure 3 in Computed Tomography). As with skeletal muscle, it is costly and labour intensive to take measures of visceral fat using multiple images. Consequently, visceral fat is normally assessed using a single MRI or CT image at L4-L5 (Figure 3). However, due to differences between the methods, visceral fat values as determined by CT are not necessarily comparable to those determined by MRI [17,18].

As discussed in the Computed Tomography section, there is a growing literature demonstrating the importance of visceral fat as a strong predictor of numerous metabolic abnormalities [19-30]. Prospective studies have shown that visceral fat predicts future hypertension [28] and type 2 diabetes [30] independent of factors such as age, BMI, weekly energy expenditure, and metabolic risk factors. Visceral fat has also recently been reported to increase risk for all-cause mortality [31]. Given the harmful effects of visceral fat [32], individuals should take steps to reduce their visceral fat and ensure it does not increase. However, as it is not possible to routinely measure visceral fat using MRI and CT, emphasis should be placed on routine measurement of waist circumference, which is the best surrogate measure currently available [33].

Figure 3:
Greyscale non-segmented and segmented magnetic resonance imaging (MRI) abdominal image at L4-L5 in an obese woman

Measuring Ectopic Fat Using MRI

MRI can also be used to assess ectopic fat deposition within the muscle and liver [34,35]. The Dixon method is most commonly used to measure fatty infiltration by MRI. In essence, the protons in fat and water produce different signals, which means the fat signal intensity of a given region relative to its water signal intensity can be used as a marker of lipid infiltration. Using this method, MRI cannot separate the lipid into its intra- and extra-cellular lipid compartments. This is not a concern in the liver as lipid exists only within the cell. In muscle, however, lipid exists both inside and outside the cell. This may be important as lipid accumulation can have different metabolic consequences depending on whether it is inside or outside the muscle cell. Nevertheless, MRI measures of lipid accumulation within the muscle and liver closely match skeletal muscle and liver intracellular lipid measures using criterion methods such as biopsy [35] or magnetic resonance spectroscopy [36,37].

MRI is one of the criterion methods for measuring visceral fat and skeletal muscle mass. It can also be used to assess lipid infiltration in tissues such as muscle and the liver. However, assessing body composition using MRI is an expensive, time-consuming, and labour-intensive process. In addition, MRI’s limited availability hinders the routine use of this tool for assessing body composition and predicting obesity-related health risk in clinical practice.

References

  1. Heymsfield SB, Lohman TG, Wang Z and Going SB. Human Body Composition. Human Kinetics Press: Champaign, IL , 2005.

    PubMed ID:

  2. Ross R, Goodpaster B, Kelley D, et al. Magnetic resonance imaging in human body composition research. From quantitative to qualitative tissue measurement. Ann N Y Acad Sci 2000; 904: 12-7.

    PubMed ID: 10865704

  3. Ross R, Rissanen J, Pedwell H, et al. Influence of diet and exercise on skeletal muscle and visceral adipose tissue in men. J Appl Physiol 1996; 81: 2445-55.

    PubMed ID: 9018491

  4. Ross R. Magnetic resonance imaging provides new insights into the characterization of adipose and lean tissue distribution. Can J Physiol Pharmacol 1996; 74: 778-85.

    PubMed ID: 8909791

  5. Thomas EL, Saeed N, Hajnal JV, et al. Magnetic resonance imaging of total body fat. J Appl Physiol 1998; 85: 1778-85.

    PubMed ID: 9804581

  6. Ross R, Léger L, Morris D, et al. Quantification of adipose tissue by MRI: relationship with anthropometric variables. J Appl Physiol 1992; 72: 787-95.

    PubMed ID: 1559959

  7. Abate N, Burns D, Peshock RM, et al. Estimation of adipose tissue mass by magnetic resonance imaging: validation against dissection in human cadavers. J Lipid Res 1994; 35: 1490-6.

    PubMed ID: 7989873

  8. Kvist H, Sjostrom L and Tylen U. Adipose tissue volume determinations in women by computed tomography: technical considerations. Int J Obes 1986; 10: 53-67.

    PubMed ID: 3710689

  9. Shen W, Wang Z, Tang H, et al. Volume estimates by imaging methods: model comparisons with visible woman as the reference. Obes Res 2003; 11: 217-25.

    PubMed ID: 12582217

  10. Snyder WS, Cooke MJ, Manssett ES, Larhansen LT, Howells GP and Tipton IH. Report of the Task Group on Reference Man. Pergamon: Oxford, UK, 1975.

    PubMed ID:

  11. Gallagher D, Belmonte D, Deurenberg P, et al. Organ-tissue mass measurement allows modeling of REE and metabolically active tissue mass. Am J Physiol 1998; 275: E249-58.

    PubMed ID: 9688626

  12. Hughes VA, Frontera WR, Wood M, et al. Longitudinal muscle strength changes in older adults: influence of muscle mass, physical activity, and health. J Gerontol A Biol Sci Med Sci 2001; 56: B209-17.

    PubMed ID: 11320101

  13. Newman AB, Haggerty CL, Goodpaster B, et al. Strength and muscle quality in a well-functioning cohort of older adults: the Health, Aging and Body Composition Study. J Am Geriatr Soc 2003; 51: 323-30.

    PubMed ID: 12588575

  14. Visser M, Goodpaster BH, Kritchevsky SB, et al. Muscle mass, muscle strength, and muscle fat infiltration as predictors of incident mobility limitations in well-functioning older persons. J Gerontol A Biol Sci Med Sci 2005; 60: 324-33.

    PubMed ID: 15860469

  15. Mitsiopoulos N, Baumgartner RN, Heymsfield SB, et al. Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. J Appl Physiol 1998; 85: 115-22.

    PubMed ID: 9655763

  16. Lee SJ, Janssen I, Heymsfield SB, et al. Relation between whole-body and regional measures of human skeletal muscle. Am J Clin Nutr 2004; 80: 1215-21.

    PubMed ID: 15531668

  17. Seidell JC, Bakker CJ and van der Kooy K. Imaging techniques for measuring adipose-tissue distribution–a comparison between computed tomography and 1.5-T magnetic resonance. Am J Clin Nutr 1990; 51: 953-7.

    PubMed ID: 2349931

  18. Ohsuzu F, Kosuda S, Takayama E, et al. Imaging techniques for measuring adipose-tissue distribution in the abdomen: a comparison between computed tomography and 1.5-tesla magnetic resonance spin-echo imaging. Radiat Med 1998; 16: 99-107.

    PubMed ID: 9650896

  19. Rennie KL, McCarthy N, Yazdgerdi S, et al. Association of the metabolic syndrome with both vigorous and moderate physical activity. Int J Epidemiol 2003; 32: 600-6.

    PubMed ID: 12913036

  20. Ekelund U, Griffin SJ and Wareham NJ. Physical activity and metabolic risk in individuals with a family history of type 2 diabetes. Diabetes Care 2007; 30: 337-42.

    PubMed ID: 17259504

  21. Carroll S, Cooke CB and Butterly RJ. Metabolic clustering, physical activity and fitness in nonsmoking, middle-aged men. Med Sci Sports Exerc 2000; 32: 2079-86.

    PubMed ID: 11128855

  22. Lakka TA, Laaksonen DE, Lakka HM, et al. Sedentary lifestyle, poor cardiorespiratory fitness, and the metabolic syndrome. Med Sci Sports Exerc 2003; 35: 1279-86.

    PubMed ID: 12900679

  23. Thune I, Njolstad I, Lochen ML, et al. Physical activity improves the metabolic risk profiles in men and women: the Tromso Study. Arch Intern Med 1998; 158: 1633-40.

    PubMed ID: 9701097

  24. Lemieux I, Pascot A, Lamarche B, et al. Is the gender difference in LDL size explained by the metabolic complications of visceral obesity? Eur J Clin Invest 2002; 32: 909-17.

    PubMed ID: 12534450

  25. Kanaley JA, Sames C, Swisher L, et al. Abdominal fat distribution in pre- and postmenopausal women: The impact of physical activity, age, and menopausal status. Metabolism 2001; 50: 976-82.

    PubMed ID: 11474488

  26. Lemieux S, Prud’homme D, Nadeau A, et al. Seven-year changes in body fat and visceral adipose tissue in women. Association with indexes of plasma glucose-insulin homeostasis. Diabetes Care 1996; 19: 983-91.

    PubMed ID: 8875093

  27. Brochu M, Starling RD, Tchernof A, et al. Visceral adipose tissue is an independent correlate of glucose disposal in older obese postmenopausal women. J Clin Endocrinol Metab 2000; 85: 2378-84.

    PubMed ID: 10902782

  28. Hayashi T, Boyko EJ, Leonetti DL, et al. Visceral adiposity is an independent predictor of incident hypertension in Japanese Americans. Ann Intern Med 2004; 140: 992-1000.

    PubMed ID: 15197016

  29. Fujimoto WY, Bergstrom RW, Boyko EJ, et al. Visceral adiposity and incident coronary heart disease in Japanese-American men. The 10-year follow-up results of the Seattle Japanese-American Community Diabetes Study. Diabetes Care 1999; 22: 1808-12.

    PubMed ID: 10546012

  30. Boyko EJ, Fujimoto WY, Leonetti DL, et al. Visceral adiposity and risk of type 2 diabetes: a prospective study among Japanese Americans. Diabetes Care 2000; 23: 465-71.

    PubMed ID: 10857936

  31. Kuk JL, Katzmarzyk PT, Nichaman MZ, et al. Visceral fat is an independent predictor of all-cause mortality in men. Obesity (Silver Spring) 2006; 14: 336-41.

    PubMed ID: 16571861

  32. Neeland IJ, Ross, R, Després JP, et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol 2019; 7: 715-25.

    PubMed ID: 31301983

  33. Ross R, Neeland IJ, Yamashita S, et al. Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat Rev Endocrinol 2020; 16: 177-89.

    PubMed ID: 32020062

  34. Kovanlikaya A, Mittelman SD, Ward A, et al. Obesity and fat quantification in lean tissues using three-point Dixon MR imaging. Pediatr Radiol 2005; 35: 601-7.

    PubMed ID: 15785930

  35. Marks SJ, Moore NR, Ryley NG, et al. Measurement of liver fat by MRI and its reduction by dexfenfluramine in NIDDM. Int J Obes Relat Metab Disord 1997; 21: 274-9.

    PubMed ID: 9130023

  36. Schick F, Machann J, Brechtel K, et al. MRI of muscular fat. Magn Reson Med 2002; 47: 720-7.

    PubMed ID: 11948733

  37. Fishbein M, Castro F, Cheruku S, et al. Hepatic MRI for fat quantitation: its relationship to fat morphology, diagnosis, and ultrasound. J Clin Gastroenterol 2005; 39: 619-25.

    PubMed ID: 16000931
Reference 1 CLOSECLOSE

Heymsfield SB, Lohman TG, Wang Z and Going SB. Human Body Composition. Human Kinetics Press: Champaign, IL , 2005.

PubMed ID:
Reference 2 CLOSECLOSE

Ross R, Goodpaster B, Kelley D, et al. Magnetic resonance imaging in human body composition research. From quantitative to qualitative tissue measurement. Ann N Y Acad Sci 2000; 904: 12-7.

PubMed ID: 10865704
Reference 3 CLOSECLOSE

Ross R, Rissanen J, Pedwell H, et al. Influence of diet and exercise on skeletal muscle and visceral adipose tissue in men. J Appl Physiol 1996; 81: 2445-55.

PubMed ID: 9018491
Reference 4 CLOSECLOSE

Ross R. Magnetic resonance imaging provides new insights into the characterization of adipose and lean tissue distribution. Can J Physiol Pharmacol 1996; 74: 778-85.

PubMed ID: 8909791
Reference 5 CLOSECLOSE

Thomas EL, Saeed N, Hajnal JV, et al. Magnetic resonance imaging of total body fat. J Appl Physiol 1998; 85: 1778-85.

PubMed ID: 9804581
Reference 6 CLOSECLOSE

Ross R, Léger L, Morris D, et al. Quantification of adipose tissue by MRI: relationship with anthropometric variables. J Appl Physiol 1992; 72: 787-95.

PubMed ID: 1559959
Reference 7 CLOSECLOSE

Abate N, Burns D, Peshock RM, et al. Estimation of adipose tissue mass by magnetic resonance imaging: validation against dissection in human cadavers. J Lipid Res 1994; 35: 1490-6.

PubMed ID: 7989873
Reference 8 CLOSECLOSE

Kvist H, Sjostrom L and Tylen U. Adipose tissue volume determinations in women by computed tomography: technical considerations. Int J Obes 1986; 10: 53-67.

PubMed ID: 3710689
Reference 9 CLOSECLOSE

Shen W, Wang Z, Tang H, et al. Volume estimates by imaging methods: model comparisons with visible woman as the reference. Obes Res 2003; 11: 217-25.

PubMed ID: 12582217
Reference 10 CLOSECLOSE

Snyder WS, Cooke MJ, Manssett ES, Larhansen LT, Howells GP and Tipton IH. Report of the Task Group on Reference Man. Pergamon: Oxford, UK, 1975.

PubMed ID:
Reference 11 CLOSECLOSE

Gallagher D, Belmonte D, Deurenberg P, et al. Organ-tissue mass measurement allows modeling of REE and metabolically active tissue mass. Am J Physiol 1998; 275: E249-58.

PubMed ID: 9688626
Reference 12 CLOSECLOSE

Hughes VA, Frontera WR, Wood M, et al. Longitudinal muscle strength changes in older adults: influence of muscle mass, physical activity, and health. J Gerontol A Biol Sci Med Sci 2001; 56: B209-17.

PubMed ID: 11320101
Reference 13 CLOSECLOSE

Newman AB, Haggerty CL, Goodpaster B, et al. Strength and muscle quality in a well-functioning cohort of older adults: the Health, Aging and Body Composition Study. J Am Geriatr Soc 2003; 51: 323-30.

PubMed ID: 12588575
Reference 14 CLOSECLOSE

Visser M, Goodpaster BH, Kritchevsky SB, et al. Muscle mass, muscle strength, and muscle fat infiltration as predictors of incident mobility limitations in well-functioning older persons. J Gerontol A Biol Sci Med Sci 2005; 60: 324-33.

PubMed ID: 15860469
Reference 15 CLOSECLOSE

Mitsiopoulos N, Baumgartner RN, Heymsfield SB, et al. Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. J Appl Physiol 1998; 85: 115-22.

PubMed ID: 9655763
Reference 16 CLOSECLOSE

Lee SJ, Janssen I, Heymsfield SB, et al. Relation between whole-body and regional measures of human skeletal muscle. Am J Clin Nutr 2004; 80: 1215-21.

PubMed ID: 15531668
Reference 17 CLOSECLOSE

Seidell JC, Bakker CJ and van der Kooy K. Imaging techniques for measuring adipose-tissue distribution–a comparison between computed tomography and 1.5-T magnetic resonance. Am J Clin Nutr 1990; 51: 953-7.

PubMed ID: 2349931
Reference 18 CLOSECLOSE

Ohsuzu F, Kosuda S, Takayama E, et al. Imaging techniques for measuring adipose-tissue distribution in the abdomen: a comparison between computed tomography and 1.5-tesla magnetic resonance spin-echo imaging. Radiat Med 1998; 16: 99-107.

PubMed ID: 9650896
Reference 19 CLOSECLOSE

Rennie KL, McCarthy N, Yazdgerdi S, et al. Association of the metabolic syndrome with both vigorous and moderate physical activity. Int J Epidemiol 2003; 32: 600-6.

PubMed ID: 12913036
Reference 20 CLOSECLOSE

Ekelund U, Griffin SJ and Wareham NJ. Physical activity and metabolic risk in individuals with a family history of type 2 diabetes. Diabetes Care 2007; 30: 337-42.

PubMed ID: 17259504
Reference 21 CLOSECLOSE

Carroll S, Cooke CB and Butterly RJ. Metabolic clustering, physical activity and fitness in nonsmoking, middle-aged men. Med Sci Sports Exerc 2000; 32: 2079-86.

PubMed ID: 11128855
Reference 22 CLOSECLOSE

Lakka TA, Laaksonen DE, Lakka HM, et al. Sedentary lifestyle, poor cardiorespiratory fitness, and the metabolic syndrome. Med Sci Sports Exerc 2003; 35: 1279-86.

PubMed ID: 12900679
Reference 23 CLOSECLOSE

Thune I, Njolstad I, Lochen ML, et al. Physical activity improves the metabolic risk profiles in men and women: the Tromso Study. Arch Intern Med 1998; 158: 1633-40.

PubMed ID: 9701097
Reference 24 CLOSECLOSE

Lemieux I, Pascot A, Lamarche B, et al. Is the gender difference in LDL size explained by the metabolic complications of visceral obesity? Eur J Clin Invest 2002; 32: 909-17.

PubMed ID: 12534450
Reference 25 CLOSECLOSE

Kanaley JA, Sames C, Swisher L, et al. Abdominal fat distribution in pre- and postmenopausal women: The impact of physical activity, age, and menopausal status. Metabolism 2001; 50: 976-82.

PubMed ID: 11474488
Reference 26 CLOSECLOSE

Lemieux S, Prud’homme D, Nadeau A, et al. Seven-year changes in body fat and visceral adipose tissue in women. Association with indexes of plasma glucose-insulin homeostasis. Diabetes Care 1996; 19: 983-91.

PubMed ID: 8875093
Reference 27 CLOSECLOSE

Brochu M, Starling RD, Tchernof A, et al. Visceral adipose tissue is an independent correlate of glucose disposal in older obese postmenopausal women. J Clin Endocrinol Metab 2000; 85: 2378-84.

PubMed ID: 10902782
Reference 28 CLOSECLOSE

Hayashi T, Boyko EJ, Leonetti DL, et al. Visceral adiposity is an independent predictor of incident hypertension in Japanese Americans. Ann Intern Med 2004; 140: 992-1000.

PubMed ID: 15197016
Reference 29 CLOSECLOSE

Fujimoto WY, Bergstrom RW, Boyko EJ, et al. Visceral adiposity and incident coronary heart disease in Japanese-American men. The 10-year follow-up results of the Seattle Japanese-American Community Diabetes Study. Diabetes Care 1999; 22: 1808-12.

PubMed ID: 10546012
Reference 30 CLOSECLOSE

Boyko EJ, Fujimoto WY, Leonetti DL, et al. Visceral adiposity and risk of type 2 diabetes: a prospective study among Japanese Americans. Diabetes Care 2000; 23: 465-71.

PubMed ID: 10857936
Reference 31 CLOSECLOSE

Kuk JL, Katzmarzyk PT, Nichaman MZ, et al. Visceral fat is an independent predictor of all-cause mortality in men. Obesity (Silver Spring) 2006; 14: 336-41.

PubMed ID: 16571861
Reference 32 CLOSECLOSE

Neeland IJ, Ross, R, Després JP, et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol 2019; 7: 715-25.

PubMed ID: 31301983
Reference 33 CLOSECLOSE

Ross R, Neeland IJ, Yamashita S, et al. Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat Rev Endocrinol 2020; 16: 177-89.

PubMed ID: 32020062
Reference 34 CLOSECLOSE

Kovanlikaya A, Mittelman SD, Ward A, et al. Obesity and fat quantification in lean tissues using three-point Dixon MR imaging. Pediatr Radiol 2005; 35: 601-7.

PubMed ID: 15785930
Reference 35 CLOSECLOSE

Marks SJ, Moore NR, Ryley NG, et al. Measurement of liver fat by MRI and its reduction by dexfenfluramine in NIDDM. Int J Obes Relat Metab Disord 1997; 21: 274-9.

PubMed ID: 9130023
Reference 36 CLOSECLOSE

Schick F, Machann J, Brechtel K, et al. MRI of muscular fat. Magn Reson Med 2002; 47: 720-7.

PubMed ID: 11948733
Reference 37 CLOSECLOSE

Fishbein M, Castro F, Cheruku S, et al. Hepatic MRI for fat quantitation: its relationship to fat morphology, diagnosis, and ultrasound. J Clin Gastroenterol 2005; 39: 619-25.

PubMed ID: 16000931