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Validation of volumetric and single-slice MRI adipose analysis using a novel fully automated segmentation method

Authors

  • Bryan T. Addeman BSc,

    1. Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada
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  • Shelby Kutty MD,

    1. University of Nebraska Medical Center, Omaha, Nebraska, USA
    2. Children's Hospital & Medical Center, Omaha, Nebraska, USA
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  • Thomas G. Perkins PhD,

    1. University of Nebraska Medical Center, Omaha, Nebraska, USA
    2. Philips Healthcare, Cleveland, Ohio, USA
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  • Abraam S. Soliman MSc,

    1. Biomedical Engineering, University of Western Ontario, London, Ontario, Canada
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  • Curtis N. Wiens MSc,

    1. Department of Physics and Astronomy, University of Western Ontario, London, Ontario, Canada
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  • Colin M. McCurdy BSc,

    1. Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada
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  • Melanie D. Beaton MD,

    1. Department of Medicine, Division of Gastroenterology, University of Western Ontario, London, Ontario, Canada
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  • Robert A. Hegele MD,

    1. Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
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  • Charles A. McKenzie PhD

    Corresponding author
    1. Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada
    2. Biomedical Engineering, University of Western Ontario, London, Ontario, Canada
    3. Department of Physics and Astronomy, University of Western Ontario, London, Ontario, Canada
    4. Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
    • Address reprint requests to: C.A.M., University of Western Ontario, Dept. of Medical Biophysics, Natural Science Room 9, 1151 Richmond St., London, Ontario, Canada N6A 5B7. E-mail: cmcken@uwo.ca

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Abstract

Purpose

To validate a fully automated adipose segmentation method with magnetic resonance imaging (MRI) fat fraction abdominal imaging. We hypothesized that this method is suitable for segmentation of subcutaneous adipose tissue (SAT) and intra-abdominal adipose tissue (IAAT) in a wide population range, easy to use, works with a variety of hardware setups, and is highly repeatable.

Materials and Methods

Analysis was performed comparing precision and analysis time of manual and automated segmentation of single-slice imaging, and volumetric imaging (78–88 slices). Volumetric and single-slice data were acquired in a variety of cohorts (body mass index [BMI] 15.6–41.76) including healthy adult volunteers, adolescent volunteers, and subjects with nonalcoholic fatty liver disease and lipodystrophies. A subset of healthy volunteers was analyzed for repeatability in the measurements.

Results

The fully automated segmentation was found to have excellent agreement with manual segmentation with no substantial bias across all study cohorts. Repeatability tests showed a mean coefficient of variation of 1.2 ± 0.6% for SAT, and 2.7 ± 2.2% for IAAT. Analysis with automated segmentation was rapid, requiring 2 seconds per slice compared with 8 minutes per slice with manual segmentation.

Conclusion

We demonstrate the ability to accurately and rapidly segment regional adipose tissue using fat fraction maps across a wide population range, with varying hardware setups and acquisition methods. J. Magn. Reson. Imaging 2015;41:233–241. © 2014 Wiley Periodicals, Inc.

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