Topography mapping of whole body adipose tissue using A fully automated and standardized procedure

Authors

  • Christian Würslin Dipl-Ing,

    Corresponding author
    1. Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany
    2. Chair of System Theory and Signal Processing, University of Stuttgart, Stuttgart, Germany
    • Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, Hoppe- Seyler-Str. 3, 72076 Tübingen, Germany
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  • Jürgen Machann Dipl-Phys,

    1. Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany
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  • Hansjörg Rempp MD,

    1. Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany
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  • Claus Claussen MD,

    1. Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany
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  • Bin Yang PhD,

    1. Chair of System Theory and Signal Processing, University of Stuttgart, Stuttgart, Germany
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  • Fritz Schick MD, PhD

    1. Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany
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Abstract

Purpose:

To obtain quantitative measures of human body fat compartments from whole body MR datasets for the risk estimation in subjects prone to metabolic diseases without the need of any user interaction or expert knowledge.

Materials and Methods:

Sets of axial T1-weighted spin-echo images of the whole body were acquired. The images were segmented using a modified fuzzy c-means algorithm. A separation of the body into anatomic regions along the body axis was performed to define regions with visceral adipose tissue present, and to standardize the results. In abdominal image slices, the adipose tissue compartments were divided into subcutaneous and visceral compartments using an extended snake algorithm. The slice-wise areas of different tissues were plotted along the slice position to obtain topographic fat tissue distributions.

Results:

Results from automatic segmentation were compared with manual segmentation. Relatively low mean deviations were obtained for the class of total tissue (4.48%) and visceral adipose tissue (3.26%). The deviation of total adipose tissue was slightly higher (8.71%).

Conclusion:

The proposed algorithm enables the reliable and completely automatic creation of adipose tissue distribution profiles of the whole body from multislice MR datasets, reducing whole examination and analysis time to less than half an hour. J. Magn. Reson. Imaging 2010; 31: 430–439. © 2010 Wiley-Liss, Inc.

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