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

Abstract

Objective The purpose of this article is to determine the feasibility of using computer assisted diagnosis (CAD) techniques to automatically identify, localize, and measure body fat tissue from a rapid wholebody MRI examination Design Prospective investigative study.Setting Dublin, Ireland.Participants 42 volunteers (21 male, 21 female) aged 18 to 56. The study group consisted of healthy volunteers with a wide range of body weights, and included a cohort of high performance athletes. Main outcome measuresThe provision of an automated system, which assesses subjects' whole body MRI scans and which provides numerical and visual feedback to illustrate the findings. The system generates results in a matter of minutes allowing for an initial assessment to be performed immediately following the completion of an MRI scan. By highlighting areas where body fat is concentrated the system allows radiologists to quickly identify and examine regions of interest in the scan.Results Rapid, accurate, and reproducible delineation of body fat distribution can be obtained using whole body MRI techniques and Computer Aided Diagnosis.Conclusions Whole-body MRI in conjunction with CAD allows a fast, automatic, and accurate approach to body fat measurement and localization and can be a useful alternative to body mass index.Whole-body fat analysis can be achieved in less than 5 min.3

Quantitative comparison and evaluation of software packages for assessment of abdominal adipose tissue distribution by magnetic resonance imaging

“…TAT was calculated by adding SAT and VAT (TAT ¼ SAT þ VAT); therefore, some small intramuscular deposits of adipose tissue were not accounted for. Similar to other published reports, 5,7,18,22,25,29,[33][34][35][36][37][40][41][42][43][44][45]47,[49][50][51][52][53][54][55][56][57][58][59][60][61][62][63] VAT and SAT were not further subdivided.…”

“…The segmentation routine can also include data pre-processing steps like de-noising and smoothing of the MR images to facilitate segmentation. 40 Semi-automated approaches support the user by determining a histogram-based threshold, 30,[41][42][43][44][45][46] boundary enhancement, 47 histogram-based region growing 41,47 or clustering. 40 Hybrid algorithms, which incorporate anatomical knowledge with segmentation algorithms, 32,33,38,39 have been successfully used to segment high contrast, artifact-free images.…”

Automated and reproducible segmentation of visceral and subcutaneous adipose tissue from abdominal MRI

“…However, signal intensity from liver has been reported to overlap the signal intensity of AT in T1 weighted images. 6,21,22 Hence, appropriate action needs to be taken to allow fully automated analysis. Acquisition of a larger volume might also require reduced image quality or multiple breath holds.…”

Test-retest reliability of rapid whole body and compartmental fat volume quantification on a widebore 3T MR system in normal-weight, overweight, and obese subjects

“…Measurement of gross body fat in separate compartments was originally very timeconsuming because of the need to manually segment the different compartments. More recently introduced semi-automated and automated segmentation techniques have made this process faster (13)(14)(15)(16)(17)and there is increasing evidence of good reliability and correlation with the laborious standard of manual segmentation (18). Variances of up to 1% for semiautomated segmentation of superficial fat, and 1-2% for intramuscular adipose tissue (19) have been reported and test-retest reliability appears to be excellent with a study of visceral and subcutaneous adipose tissue in children using a fully automated process demonstrating correlation coefficients of r=0.977 for VAT and r=0.999 for subcutaneous adipose tissue (SAT) (20).…”