- Email: [email protected]
Fast adipose tissue (FAT) assessment by MRI
Magnetic Resonance Imaging 18 (2000) 815– 818
Fast adipose tissue (FAT) assessment by MRI夞 Suzanne A. Gronemeyera,b,*, R. Grant Steena– d, William M. Kauffmana,b, W. Eugene Reddicka,e,f, John O. Glassa a
Department of Diagnostic Imaging, St. Jude’s Children’s Research Hospital, Memphis, TN, USA b Department of Radiology, University of Tennessee School of Medicine, Memphis, TN, USA c Department of Pediatrics, University of Tennessee School of Medicine, Memphis, TN, USA d Department of Biomedical Engineering, University of Tennessee School of Medicine, Memphis, TN, USA e Department of Electrical Engineering, University of Memphis, Memphis, TN, USA f Department of Biomedical Engineering, University of Memphis, Memphis, TN, USA Received 1 June 1999; received in revised form 4 June 2000
Abstract We report a method of fast adipose tissue (FAT) assessment to characterize the quantity, and distribution of abdominal adipose tissue. Whole-volume coverage of the abdomen was obtained using 31 contiguous transverse T1-weighted images from 16 obese females. A radiologist manually traced all adipose tissue volumes in the images, while a physiologist used an automated method to measure adipose tissue in a single image at the level of the umbilicus. Automated analysis of the umbilicus-level image was significantly correlated with values obtained by manual analysis of the entire abdomen (p ⬍ 0.001). There was good agreement between the automated umbilicus-level image method and the manual whole abdomen method for subcutaneous adipose tissue (r 2 ⫽ 0.958), visceral adipose tissue (r 2 ⫽ 0.753), and total adipose tissue (r 2 ⫽ 0.941). The automated method required 6 min vs 2 h for the manual method. © 2000 Elsevier Science Inc. All rights reserved. Keywords: Adipose tissue, Obesity, Anthropometry, Body mass index
1. Introduction Body composition is an important health risk factor, yet body composition can be difficult to characterize. Accurate measurement of body composition requires underwater weighing [1], which is inappropriate for children, the elderly, or the very ill. Simple methods to estimate body composition, such as skin-fold thickness and anthropometric measurements, are imprecise and prone to bias in certain populations [2,3]. For example, when standard equations derived from a Caucasian population were used to calculate African-American body composition, results were erroneous [4], in part because of the greater bone mineral density of African-Americans [2,3]. It is especially difficult to characterize the volume and distribution of visceral adipose
夞 Supported in part by the National Cancer Institute Cancer Center Support CORE grant P30CA21765 and by the American Lebanese Syrian Associated Charities (ALSAC). * Corresponding author. Tel.: ⫹1-901-495-2488; fax: ⫹1-901-4953292. E-mail address: [email protected] (S.A. Gronemeyer).
tissue (VAT), which is the fat deposited within the abdominal cavity around the internal organs. High levels of VAT have been linked to an increased risk of obesity-related illnesses, such as diabetes [5,6]. Yet, to date, there is no simple and reliable method to measure VAT [7]. Magnetic resonance imaging (MRI) may provide a method to measure adipose tissue safely and accurately. It does not require water submersion or radiation exposure, it can be done rapidly, it provides images which are also useful for clinical diagnosis, and it has been used to characterize adipose tissue volumes [8 –13]. However, previous MRI methods were either laborious or subject to technical problems [8 –13]. Here we report a rapid, automated method to measure VAT, subcutaneous adipose tissue (SAT), and total adipose tissue (TAT) volumes. This analysis can be performed in about 6 min by a non-radiologist, using images that are often collected during a standard MRI exam. We tested a hypothesis that this rapid, automated method could provide a surrogate measure with acceptable precision to replace the laborious manual measurement of MR images of the abdomen.
0730-725X/00/$ – see front matter © 2000 Elsevier Science Inc. All rights reserved. PII: S 0 7 3 6 - 7 2 5 X ( 0 0 ) 0 0 1 6 8 - 5
816
S.A. Gronemeyer et al. / Magnetic Resonance Imaging 18 (2000) 815– 818
2. Methods 2.1. Subjects and MRI Sixteen female subjects (aged 19 – 44 years; median ⫽ 32) underwent whole-abdomen MR imaging. Informed consent was obtained from all subjects. Subjects were imaged on a 1.5T Magnetom SP63A (Siemens Medical Systems, Iselin, NJ) scanner using a standard body coil. The images were obtained in two interleaved sets, one of 15 and one of 16 images. Each set was obtained with a 100% inter-slice gap to yield 31 contiguous 10-mm axial T1-weighted spinecho images of the entire abdomen (repetition time TR ⫽ 700 ms, echo time TE ⫽ 15 ms, 90° flip angle, 4 excitations). The field of view (FOV) was 50 cm. The matrix and rectangular FOV factor was 128*256 and 4/8 for moderately obese subjects, and was 256*256 and 8/8 for very obese subjects. In-plane resolution was 1.95 mm ⫻ 1.95 mm, with a voxel volume of 38 mm3. The imaging time was 12 min for moderately obese subjects and 24 min for very obese subjects. 2.2. Manual image analysis Images were transferred to a Macintosh IIvx computer for manual assessment of SAT, VAT, and TAT deposition. A radiologist (WMK) manually selected SAT and VAT in each of the 31 contiguous images, using the Photoshop威 “magic wand” feature to outline regions of similar signal intensity. In regions of fat deposition with poor fat-tissue contrast, margins were drawn manually around the fat deposition. Pixel counts were multiplied by the MRI voxel size to obtain compartment volumes. The SAT ⫹ VAT volumes for the 31 images were summed to obtain TAT volume. Manual measurement of fat deposits in all 31 images took approximately 2 h per subject. 2.3. Automated image analysis A non-radiologist (RGS) performed an automated analysis of a single image at the level of the umbilicus using the same Photoshop威 software (Fig. 1). The subject’s arms were erased using the “eraser” tool (Fig. 1A). Then all the intra-abdominal tissue was erased (Fig. 1C). A “threshold tool” was then used to select a threshold signal intensity value (Fig. 1D) above which SAT was white and all other tissues were black (Fig. 1E). These pixels were scored as SAT. This threshold value was then applied to the initial umbilicus-level image (Fig. 1A), and all pixels exceeding the threshold value (Fig. 1F) were scored as TAT. Then: VAT ⫽ TAT ⫺ SAT The entire process required approximately 6 min per subject.
Fig. 1. Transverse T1W umbilicus-level MR image analysis using Adobe Photoshop威; (A) The subject’s arms were erased; (B) Histogram for the resultant image; (C) All intra-abdominal tissues were erased; (D) Histogram after the intra-abdominal tissues have been erased; (E) A threshold value was selected for Fig. 1C above which the signal intensity of adipose tissue was white and all other tissues were blacked out; (F) This threshold value was then applied to Fig. 1A. Pixels exceeding the threshold value were scored as subcutaneous or visceral adipose tissue (SAT or VAT).
2.4. Statistical analysis All statistical analyses relied upon the SPSS 6.1 statistical package (SPSS Inc., Chicago, IL), running on a PowerCenter Pro 180 (Power Computing, Round Rock, TX). The tests used were either the paired-samples t-test or an ANOVA correlation.
S.A. Gronemeyer et al. / Magnetic Resonance Imaging 18 (2000) 815– 818
817
Table 1 Comparison of adipose tissue pixels in umbilicus-level MRI counted by the automated and manual methods Comparison
Correlation coefficient r
Percent r2
Manual mean (⫾ SD)
Automated mean (⫾ SD)
Significance of correlation
SAT VAT TAT
0.998 0.885 0.990
99.5 78.4 97.9
13.27 (⫾4.51) 2.81 (⫾1.43) 16.08 (⫾5.59)
12.72 (⫾4.31) 4.18 (⫾2.50) 16.89 (⫾6.55)
p ⬍ 0.0001 p ⬍ 0.0001 p ⬍ 0.0001
The correlation between umbilicus-level pixels in subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and total adipose tissue (TAT), counted by the automated and manual methods in 16 subjects. The mean number of fat pixels (in units of 1,000 ⫾ standard deviation, SD) counted per subject is shown. Although a Student’s t-test for paired data showed that automated pixel count values were significantly different from the manual pixel count values (all p ⬍ 0.001), the correlation between the two methods was significant for SAT, VAT, and TAT.
3. Results
4. Discussion
Manual measurement of adipose tissue over the whole abdomen in 16 volunteers was significantly correlated with manual measurement of adipose tissue in a single image at the level of the umbilicus (SAT r 2 ⫽ 0.965; VAT r 2 ⫽ 0.932; TAT r 2 ⫽ 0.970; correlation p ⬍ 0.0001). This shows that the single umbilicus-level image values are an excellent surrogate measure for whole-abdomen values in this cohort. Volumes obtained by manual and automated assessment of SAT, VAT, and TAT in the single image differed significantly from each other (p ⬍ 0.001), but they were significantly correlated (SAT r 2 ⫽ 0.995; VAT r 2 ⫽ 0.784; TAT r 2 ⫽ 0.979; correlation p ⬍ 0.0001). Because variation in the manual method explains roughly 92% of the variation in the automated method (Table 1), it should be possible to correct the automated values to give a close approximation of the manual values. Finally, the volumes of SAT, VAT, and TAT measured by the manual multi-slice method and by the automated single slice method were significantly correlated (SAT r 2 ⫽ 0.958; VAT r 2 ⫽ 0.753; TAT r 2 ⫽ 0.941; correlation p ⬍ 0.0001). Therefore, the automated single-image values are an adequate surrogate for whole-abdomen values of SAT, VAT, and TAT. Erasure of the intra-abdominal tissues (Fig. 1) before selection of a threshold value for SAT greatly simplified data analysis, possibly because internal tissues are visually confusing. Comparison of the image histograms of the umbilicus-level image before (Fig. 1B) and after (Fig. 1D) the intra-abdominal tissues were erased show that most of the intermediate signal intensity regions arose from internal organs such as the intestines. The automated analysis was repeated after 2 months to evaluate the precision of the method. The operator (RGS) was blinded to the results obtained earlier. The correlations for the first and second evaluations were high (r 2 ⫽ 0.996 for SAT, r 2 ⫽ 0.933 for VAT, and r 2 ⫽ 0.984 for TAT; all p ⬍ 0.0001). Somewhat different thresholds were chosen in the two analyses, but pixel counts in the two analyses differed by only 0.2% for SAT, 10.8% for VAT and 2.8% for TAT. The automated method was relatively insensitive to the threshold chosen; therefore, this method has acceptable precision.
We have shown that automated analysis of a single image at the level of the umbilicus can be used to characterize SAT, VAT, and TAT over the whole abdomen. Our results are consistent with a recent study that showed that abdominal adipose tissue in men could be accurately measured from a single image at the same level [12]. However, extrapolation of the single-slice value to the whole abdomen must be validated for the particular cohort of interest. Our method offers several advantages over existing methodologies. The gold standard of fat assessment is underwater weighing or impedance measurements. Both methods require equipment that is not widely available in the hospital environment. By contrast, most hospitals have an MRI scanner. Computed tomography (CT) may not be as good as MRI for characterizing VAT because the CT signal intensity of fat is very low, similar to that of air and bowel gas [14]. The bright signal intensity of fat on T1-weighted MRI and the absence of signal from air, and bowel gas on MRI permits the distinction between fat, air, and other tissues. Because the automated method is so rapid, it has a number of potential applications. Body composition studies could be performed concurrently with a diagnostic MRI exam, adding at most a few min to the scan time. This may make it possible to detect early-onset cachexia in cancer patients [14], and thus enable nutritional intervention before overt clinical signs emerge. On the newest MRI scanners, it is possible to perform whole-abdomen breath-hold T1W gradient echo imaging in under a min. Acknowledgment We thank Abbas E. Kitabchi, Ph.D., M.D. and Beverly Williams-Cleaves, M.D., of the University of Tennessee— Memphis, Division of Endocrinology and Metabolism, for referring subjects to us. References [1] Van Loan MD. Is dual-energy X-ray absorptiometry ready for prime time in the clinical evaluation of body composition? [editorial]. Am J Clin Nutr 1998;68:1155– 6.
818
S.A. Gronemeyer et al. / Magnetic Resonance Imaging 18 (2000) 815– 818
[2] Ortiz O, Russell M, Daley TL, Baumgartner RN, Waki M, Lichtman S, Wang J, Pierson RN Jr, Heymsfield SB. Differences in skeletal muscles and bone mineral mass between black and white females and their relevance to estimates of body composition. Am J Clin Nutr 1992;55:8 –13. [3] Schutte JE, Townsend EJ, Hugg J, Shoup RF, Malina RM, Blomqvist CG. Density of lean body mass is greater in blacks than in whites. J Appl Physiol 1984;56:1647–9. [4] Brandon LJ. Comparison of existing skinfold equations for estimating body fat in African American and white women. Am J Clin Nutr 1998;67:1155– 61. [5] Kitabchi AE, Buffington CK. Body fat distribution, hyperandrogenicity, and health risks. Semin Reprod Endocrinol 1994:6 –14. [6] Conway JM, Yanovski SZ, Avila NA, Hubbard VS. Visceral adipose tissue differences in black and white women. Am J Clin Nutr 1995; 61:765–71. [7] Abate N, Burns D, Peshock RM, Garg A, Grundy SM. Estimation of adipose tissue mass by magnetic resonance imaging: validation against dissection in human cadavers. J Lipid Res 1994;35:1490 – 6. [8] Gray DS, Fujioka K, Colletti PM, Kim H, Devine D, Cuyegkeng T, Pappas T. Magnetic resonance imaging used for determining fat distribution in obesity and diabetes. Am J Clin Nutr 1991;54:623–7.
[9] Fowler PA, Fuller MF, Glasbey CA, Foster MA, Cameron CG, McNeill G, Maughan RJ. Total and subcutaneous adipose tissue in women: the measurement of distribution and accurate prediction of quantity by using magnetic resonance imaging. Am J Clin Nutr 1991;54:18 –25. [10] Abate N, Garg A, Peshock RM, Stray-Gundersen J, Grundy SM. Relationships of generalized and regional adiposity to insulin sensitivity in men. J Clin Invest 1995;96:88 –98. [11] Eliakim A, Burke GS, Cooper DM. Fitness, fatness, and the effect of training assessed by magnetic resonance imaging and skinfold-thickness measurements in healthy adolescent females. Am J Clin Nutr 1997;66:223–31. [12] Abate N, Garg A, Coleman R, Grundy SM, Peshock RM. Prediction of total subcutaneous abdominal, intraperitoneal, and retroperitoneal adipose tissue masses in men by a single axial magnetic resonance imaging slice. Am J Clin Nutr 1997;65:403– 8. [13] Sobol W, Rossner S, Hinson B, Hiltbrandt E, Karstaedt N, Santago P, Wolfman N, Hagaman A, 3d, CJ. Evaluation of a new magnetic resonance imaging method for quantitating adipose tissue areas. Int J Obes 1991;15:589 –99. [14] Cheney CL, Mahloch J, Freeny P. Computerized tomography assessment of women with weight changes associated with adjuvant treatment for breast cancer. Am J Clin Nutr 1997;66:141– 6.
Fast adipose tissue (FAT) assessment by MRI夞 Suzanne A. Gronemeyera,b,*, R. Grant Steena– d, William M. Kauffmana,b, W. Eugene Reddicka,e,f, John O. Glassa a
Department of Diagnostic Imaging, St. Jude’s Children’s Research Hospital, Memphis, TN, USA b Department of Radiology, University of Tennessee School of Medicine, Memphis, TN, USA c Department of Pediatrics, University of Tennessee School of Medicine, Memphis, TN, USA d Department of Biomedical Engineering, University of Tennessee School of Medicine, Memphis, TN, USA e Department of Electrical Engineering, University of Memphis, Memphis, TN, USA f Department of Biomedical Engineering, University of Memphis, Memphis, TN, USA Received 1 June 1999; received in revised form 4 June 2000
Abstract We report a method of fast adipose tissue (FAT) assessment to characterize the quantity, and distribution of abdominal adipose tissue. Whole-volume coverage of the abdomen was obtained using 31 contiguous transverse T1-weighted images from 16 obese females. A radiologist manually traced all adipose tissue volumes in the images, while a physiologist used an automated method to measure adipose tissue in a single image at the level of the umbilicus. Automated analysis of the umbilicus-level image was significantly correlated with values obtained by manual analysis of the entire abdomen (p ⬍ 0.001). There was good agreement between the automated umbilicus-level image method and the manual whole abdomen method for subcutaneous adipose tissue (r 2 ⫽ 0.958), visceral adipose tissue (r 2 ⫽ 0.753), and total adipose tissue (r 2 ⫽ 0.941). The automated method required 6 min vs 2 h for the manual method. © 2000 Elsevier Science Inc. All rights reserved. Keywords: Adipose tissue, Obesity, Anthropometry, Body mass index
1. Introduction Body composition is an important health risk factor, yet body composition can be difficult to characterize. Accurate measurement of body composition requires underwater weighing [1], which is inappropriate for children, the elderly, or the very ill. Simple methods to estimate body composition, such as skin-fold thickness and anthropometric measurements, are imprecise and prone to bias in certain populations [2,3]. For example, when standard equations derived from a Caucasian population were used to calculate African-American body composition, results were erroneous [4], in part because of the greater bone mineral density of African-Americans [2,3]. It is especially difficult to characterize the volume and distribution of visceral adipose
夞 Supported in part by the National Cancer Institute Cancer Center Support CORE grant P30CA21765 and by the American Lebanese Syrian Associated Charities (ALSAC). * Corresponding author. Tel.: ⫹1-901-495-2488; fax: ⫹1-901-4953292. E-mail address: [email protected] (S.A. Gronemeyer).
tissue (VAT), which is the fat deposited within the abdominal cavity around the internal organs. High levels of VAT have been linked to an increased risk of obesity-related illnesses, such as diabetes [5,6]. Yet, to date, there is no simple and reliable method to measure VAT [7]. Magnetic resonance imaging (MRI) may provide a method to measure adipose tissue safely and accurately. It does not require water submersion or radiation exposure, it can be done rapidly, it provides images which are also useful for clinical diagnosis, and it has been used to characterize adipose tissue volumes [8 –13]. However, previous MRI methods were either laborious or subject to technical problems [8 –13]. Here we report a rapid, automated method to measure VAT, subcutaneous adipose tissue (SAT), and total adipose tissue (TAT) volumes. This analysis can be performed in about 6 min by a non-radiologist, using images that are often collected during a standard MRI exam. We tested a hypothesis that this rapid, automated method could provide a surrogate measure with acceptable precision to replace the laborious manual measurement of MR images of the abdomen.
0730-725X/00/$ – see front matter © 2000 Elsevier Science Inc. All rights reserved. PII: S 0 7 3 6 - 7 2 5 X ( 0 0 ) 0 0 1 6 8 - 5
816
S.A. Gronemeyer et al. / Magnetic Resonance Imaging 18 (2000) 815– 818
2. Methods 2.1. Subjects and MRI Sixteen female subjects (aged 19 – 44 years; median ⫽ 32) underwent whole-abdomen MR imaging. Informed consent was obtained from all subjects. Subjects were imaged on a 1.5T Magnetom SP63A (Siemens Medical Systems, Iselin, NJ) scanner using a standard body coil. The images were obtained in two interleaved sets, one of 15 and one of 16 images. Each set was obtained with a 100% inter-slice gap to yield 31 contiguous 10-mm axial T1-weighted spinecho images of the entire abdomen (repetition time TR ⫽ 700 ms, echo time TE ⫽ 15 ms, 90° flip angle, 4 excitations). The field of view (FOV) was 50 cm. The matrix and rectangular FOV factor was 128*256 and 4/8 for moderately obese subjects, and was 256*256 and 8/8 for very obese subjects. In-plane resolution was 1.95 mm ⫻ 1.95 mm, with a voxel volume of 38 mm3. The imaging time was 12 min for moderately obese subjects and 24 min for very obese subjects. 2.2. Manual image analysis Images were transferred to a Macintosh IIvx computer for manual assessment of SAT, VAT, and TAT deposition. A radiologist (WMK) manually selected SAT and VAT in each of the 31 contiguous images, using the Photoshop威 “magic wand” feature to outline regions of similar signal intensity. In regions of fat deposition with poor fat-tissue contrast, margins were drawn manually around the fat deposition. Pixel counts were multiplied by the MRI voxel size to obtain compartment volumes. The SAT ⫹ VAT volumes for the 31 images were summed to obtain TAT volume. Manual measurement of fat deposits in all 31 images took approximately 2 h per subject. 2.3. Automated image analysis A non-radiologist (RGS) performed an automated analysis of a single image at the level of the umbilicus using the same Photoshop威 software (Fig. 1). The subject’s arms were erased using the “eraser” tool (Fig. 1A). Then all the intra-abdominal tissue was erased (Fig. 1C). A “threshold tool” was then used to select a threshold signal intensity value (Fig. 1D) above which SAT was white and all other tissues were black (Fig. 1E). These pixels were scored as SAT. This threshold value was then applied to the initial umbilicus-level image (Fig. 1A), and all pixels exceeding the threshold value (Fig. 1F) were scored as TAT. Then: VAT ⫽ TAT ⫺ SAT The entire process required approximately 6 min per subject.
Fig. 1. Transverse T1W umbilicus-level MR image analysis using Adobe Photoshop威; (A) The subject’s arms were erased; (B) Histogram for the resultant image; (C) All intra-abdominal tissues were erased; (D) Histogram after the intra-abdominal tissues have been erased; (E) A threshold value was selected for Fig. 1C above which the signal intensity of adipose tissue was white and all other tissues were blacked out; (F) This threshold value was then applied to Fig. 1A. Pixels exceeding the threshold value were scored as subcutaneous or visceral adipose tissue (SAT or VAT).
2.4. Statistical analysis All statistical analyses relied upon the SPSS 6.1 statistical package (SPSS Inc., Chicago, IL), running on a PowerCenter Pro 180 (Power Computing, Round Rock, TX). The tests used were either the paired-samples t-test or an ANOVA correlation.
S.A. Gronemeyer et al. / Magnetic Resonance Imaging 18 (2000) 815– 818
817
Table 1 Comparison of adipose tissue pixels in umbilicus-level MRI counted by the automated and manual methods Comparison
Correlation coefficient r
Percent r2
Manual mean (⫾ SD)
Automated mean (⫾ SD)
Significance of correlation
SAT VAT TAT
0.998 0.885 0.990
99.5 78.4 97.9
13.27 (⫾4.51) 2.81 (⫾1.43) 16.08 (⫾5.59)
12.72 (⫾4.31) 4.18 (⫾2.50) 16.89 (⫾6.55)
p ⬍ 0.0001 p ⬍ 0.0001 p ⬍ 0.0001
The correlation between umbilicus-level pixels in subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and total adipose tissue (TAT), counted by the automated and manual methods in 16 subjects. The mean number of fat pixels (in units of 1,000 ⫾ standard deviation, SD) counted per subject is shown. Although a Student’s t-test for paired data showed that automated pixel count values were significantly different from the manual pixel count values (all p ⬍ 0.001), the correlation between the two methods was significant for SAT, VAT, and TAT.
3. Results
4. Discussion
Manual measurement of adipose tissue over the whole abdomen in 16 volunteers was significantly correlated with manual measurement of adipose tissue in a single image at the level of the umbilicus (SAT r 2 ⫽ 0.965; VAT r 2 ⫽ 0.932; TAT r 2 ⫽ 0.970; correlation p ⬍ 0.0001). This shows that the single umbilicus-level image values are an excellent surrogate measure for whole-abdomen values in this cohort. Volumes obtained by manual and automated assessment of SAT, VAT, and TAT in the single image differed significantly from each other (p ⬍ 0.001), but they were significantly correlated (SAT r 2 ⫽ 0.995; VAT r 2 ⫽ 0.784; TAT r 2 ⫽ 0.979; correlation p ⬍ 0.0001). Because variation in the manual method explains roughly 92% of the variation in the automated method (Table 1), it should be possible to correct the automated values to give a close approximation of the manual values. Finally, the volumes of SAT, VAT, and TAT measured by the manual multi-slice method and by the automated single slice method were significantly correlated (SAT r 2 ⫽ 0.958; VAT r 2 ⫽ 0.753; TAT r 2 ⫽ 0.941; correlation p ⬍ 0.0001). Therefore, the automated single-image values are an adequate surrogate for whole-abdomen values of SAT, VAT, and TAT. Erasure of the intra-abdominal tissues (Fig. 1) before selection of a threshold value for SAT greatly simplified data analysis, possibly because internal tissues are visually confusing. Comparison of the image histograms of the umbilicus-level image before (Fig. 1B) and after (Fig. 1D) the intra-abdominal tissues were erased show that most of the intermediate signal intensity regions arose from internal organs such as the intestines. The automated analysis was repeated after 2 months to evaluate the precision of the method. The operator (RGS) was blinded to the results obtained earlier. The correlations for the first and second evaluations were high (r 2 ⫽ 0.996 for SAT, r 2 ⫽ 0.933 for VAT, and r 2 ⫽ 0.984 for TAT; all p ⬍ 0.0001). Somewhat different thresholds were chosen in the two analyses, but pixel counts in the two analyses differed by only 0.2% for SAT, 10.8% for VAT and 2.8% for TAT. The automated method was relatively insensitive to the threshold chosen; therefore, this method has acceptable precision.
We have shown that automated analysis of a single image at the level of the umbilicus can be used to characterize SAT, VAT, and TAT over the whole abdomen. Our results are consistent with a recent study that showed that abdominal adipose tissue in men could be accurately measured from a single image at the same level [12]. However, extrapolation of the single-slice value to the whole abdomen must be validated for the particular cohort of interest. Our method offers several advantages over existing methodologies. The gold standard of fat assessment is underwater weighing or impedance measurements. Both methods require equipment that is not widely available in the hospital environment. By contrast, most hospitals have an MRI scanner. Computed tomography (CT) may not be as good as MRI for characterizing VAT because the CT signal intensity of fat is very low, similar to that of air and bowel gas [14]. The bright signal intensity of fat on T1-weighted MRI and the absence of signal from air, and bowel gas on MRI permits the distinction between fat, air, and other tissues. Because the automated method is so rapid, it has a number of potential applications. Body composition studies could be performed concurrently with a diagnostic MRI exam, adding at most a few min to the scan time. This may make it possible to detect early-onset cachexia in cancer patients [14], and thus enable nutritional intervention before overt clinical signs emerge. On the newest MRI scanners, it is possible to perform whole-abdomen breath-hold T1W gradient echo imaging in under a min. Acknowledgment We thank Abbas E. Kitabchi, Ph.D., M.D. and Beverly Williams-Cleaves, M.D., of the University of Tennessee— Memphis, Division of Endocrinology and Metabolism, for referring subjects to us. References [1] Van Loan MD. Is dual-energy X-ray absorptiometry ready for prime time in the clinical evaluation of body composition? [editorial]. Am J Clin Nutr 1998;68:1155– 6.
818
S.A. Gronemeyer et al. / Magnetic Resonance Imaging 18 (2000) 815– 818
[2] Ortiz O, Russell M, Daley TL, Baumgartner RN, Waki M, Lichtman S, Wang J, Pierson RN Jr, Heymsfield SB. Differences in skeletal muscles and bone mineral mass between black and white females and their relevance to estimates of body composition. Am J Clin Nutr 1992;55:8 –13. [3] Schutte JE, Townsend EJ, Hugg J, Shoup RF, Malina RM, Blomqvist CG. Density of lean body mass is greater in blacks than in whites. J Appl Physiol 1984;56:1647–9. [4] Brandon LJ. Comparison of existing skinfold equations for estimating body fat in African American and white women. Am J Clin Nutr 1998;67:1155– 61. [5] Kitabchi AE, Buffington CK. Body fat distribution, hyperandrogenicity, and health risks. Semin Reprod Endocrinol 1994:6 –14. [6] Conway JM, Yanovski SZ, Avila NA, Hubbard VS. Visceral adipose tissue differences in black and white women. Am J Clin Nutr 1995; 61:765–71. [7] Abate N, Burns D, Peshock RM, Garg A, Grundy SM. Estimation of adipose tissue mass by magnetic resonance imaging: validation against dissection in human cadavers. J Lipid Res 1994;35:1490 – 6. [8] Gray DS, Fujioka K, Colletti PM, Kim H, Devine D, Cuyegkeng T, Pappas T. Magnetic resonance imaging used for determining fat distribution in obesity and diabetes. Am J Clin Nutr 1991;54:623–7.
[9] Fowler PA, Fuller MF, Glasbey CA, Foster MA, Cameron CG, McNeill G, Maughan RJ. Total and subcutaneous adipose tissue in women: the measurement of distribution and accurate prediction of quantity by using magnetic resonance imaging. Am J Clin Nutr 1991;54:18 –25. [10] Abate N, Garg A, Peshock RM, Stray-Gundersen J, Grundy SM. Relationships of generalized and regional adiposity to insulin sensitivity in men. J Clin Invest 1995;96:88 –98. [11] Eliakim A, Burke GS, Cooper DM. Fitness, fatness, and the effect of training assessed by magnetic resonance imaging and skinfold-thickness measurements in healthy adolescent females. Am J Clin Nutr 1997;66:223–31. [12] Abate N, Garg A, Coleman R, Grundy SM, Peshock RM. Prediction of total subcutaneous abdominal, intraperitoneal, and retroperitoneal adipose tissue masses in men by a single axial magnetic resonance imaging slice. Am J Clin Nutr 1997;65:403– 8. [13] Sobol W, Rossner S, Hinson B, Hiltbrandt E, Karstaedt N, Santago P, Wolfman N, Hagaman A, 3d, CJ. Evaluation of a new magnetic resonance imaging method for quantitating adipose tissue areas. Int J Obes 1991;15:589 –99. [14] Cheney CL, Mahloch J, Freeny P. Computerized tomography assessment of women with weight changes associated with adjuvant treatment for breast cancer. Am J Clin Nutr 1997;66:141– 6.