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Toward an Automated Method of Abdominal Fat Segmentation of MR Images

This study has been completed.
Sponsor:
Information provided by:
Washington University School of Medicine
ClinicalTrials.gov Identifier:
NCT01228968
First received: October 25, 2010
Last updated: May 11, 2011
Last verified: May 2011
History of Changes

October 25, 2010
May 11, 2011
October 2010
February 2011   (final data collection date for primary outcome measure)
  • Visceral Fat Volume With Automated Analysis [ Time Frame: five minutes ] [ Designated as safety issue: No ]
    This is the measurement of Abdominal Visceral Fat in cubic centimeters as determined with a new automated segmentation program.
  • Visceral Fat Volume With Manual Segmentation [ Time Frame: five minutes ] [ Designated as safety issue: No ]
    This is the measure of visceral fat found with our older manual segmentation method
Visceral Fat Volume [ Time Frame: Baseline ] [ Designated as safety issue: No ]
The measurement of Abdominal Visceral Fat in cubic centimeters
Complete list of historical versions of study NCT01228968 on ClinicalTrials.gov Archive Site
  • Subcutaneous Fat Volume With Automated Analysis [ Time Frame: five minutes ] [ Designated as safety issue: No ]
    This is the volume of Abdominal Subcutaneous Fat in cubic centimeters as determined with new automated anatomical segmentation software.
  • Subcutaneous Fat Volume With Manual Segmentation [ Time Frame: five minutes ] [ Designated as safety issue: No ]
    This is the volume of Abdominal Subcutaneous Fat in cubic centimeters as determined with the older manual segmentation technique.
Subcutaneous Fat Volume [ Time Frame: baseline ] [ Designated as safety issue: No ]
The measurement of Abdominal Subcutaneous Fat in cubic centimeters
Not Provided
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Toward an Automated Method of Abdominal Fat Segmentation of MR Images
Toward an Automated Method of Abdominal Fat Segmentation of MR Images

Subjects will undergo a brief magnetic resonance (MRI) scan. The resulting images will be used to compare two abdominal fat segmentation techniques. The first technique is already validated and in use. The second technique was recently developed and has not been validated. The hypothesis is that the second technique will be the faster and more reliable of the two.
Not Provided
Observational
Observational Model: Case-Only
Time Perspective: Cross-Sectional
Not Provided
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Non-Probability Sample

Subjects will have a wide range of body mass index and other physical characteristics.
Obesity
Not Provided
Volunteers
Volunteers will have a range of body mass index from 19 - 45 kilogram per square meter. In order to fit in the magnetic resonance scanner subjects must weigh less than 300 pounds.
Not Provided

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Completed
9
February 2011
February 2011   (final data collection date for primary outcome measure)

Inclusion Criteria:

  • ambulatory
  • cognitively sound

Exclusion Criteria:

  • body mass index less than 18 or greater than 45 kilograms per square meter
Both
18 Years to 70 Years
Yes
Contact information is only displayed when the study is recruiting subjects
United States
 
NCT01228968
MRImethods060229
No
Samuel Klein, MD, Washington University School of Medicine
Washington University School of Medicine
Not Provided
Principal Investigator: Samuel Klein, M.D. Washington University School of Medicine
Washington University School of Medicine
May 2011

ICMJE     Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP