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Rapid Automated Measurement of Body Fat Distribution from Whole-Body MRI

Darren D. Brennan1, Paul F. Whelan2, Kevin Robinson2, Ovidiu Ghita2, Julie M. O'Brien1, Robert Sadleir2 and Stephen J. Eustace1

1 Department of Radiology, Cappagh National Orthopaedic Hospital, Finglas, Dublin, 11 Ireland.
2 Vision Systems Group, Dublin City University, Dublin, Ireland.



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  		Fig. 1 —28-year-old female volunteer. System display output illustrating localized regions identified as fat (marked in yellow in image on right). Slider bar allows user to step through image sequence. Image on left is original data after alignment and basic image processing to reduce noise artifacts.

 


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  		Fig. 2 —28-year-old female volunteer. Volume rendering of full-body MRI data set giving overview of patient's superficial body-fat distribution. This fully interactive 3D model allows flexible data clipping to be performed, which enables detailed analysis of regional fat distribution.

 


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  		Fig. 3 —System output text on per patient basis in 31-year-old male volunteer. Two slightly different methods for fat calculation are used. DICOM weight is that entered by the MRI technologist and is inaccurate in this patient, whose body weight was 102 kg. System provides accurate assessment of body fat. By applying clipping tool to 3D volume-rendered image (see Fig. 2), similar detailed voxel-by-voxel interrogation of particular body region can be performed.

 


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  		Fig. 4 —Comparing manually measured body mass index (BMI) and automatic computer-assisted diagnosis (CAD) calculation illustrates that CAD system reliability matches manual BMI measurements. Test was used as initial validation of segmentation procedure and shows that CAD system developed produces reliable measurements. See text for full discussion. {blacktriangleup} = best 21 data sets, • = rest of full 42 data sets, broken trend line = best 21 data sets, solid trend line = full 42 data sets.

 


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  		Fig. 5 —Body mass index (BMI) mapped against percentage body fat. Data are divided by sex, and two trend lines clearly illustrate sex difference. Men tend to have lower percentage body fat than women for same BMI. Very lean subjects from cohort of athletes examined further lift trend line for male subset because high muscle mass offsets low body fat and results in relatively high BMI values in this group, situated at extreme left of graphed data. Complexity of relationship between percentage body fat and BMI illustrates shortcomings of BMI as reliable indicator of body fat. • = men, {blacktriangleup}= women, upper trend line = men, lower trend line = women.

 

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