Literature DB >> 24900077

Optimal CT Number Range for Adipose Tissue When Determining Lean Body Mass in Whole-Body F-18 FDG PET/CT Studies.

Woo Hyoung Kim1, Chang Guhn Kim2, Dae-Weung Kim2.   

Abstract

PURPOSE: The aim of this study was to define an optimal CT number range applicable to adipose tissue (AT) measurement in modern PET/CT systems.
METHODS: CT number (in Hounsfield units, HU) was measured in three different pure AT compartments in 53 patients. CT number range for AT was determined in three different ways, including pixel histogram analysis, to take the effect of partial volume averaging into account. The effect of changing the CT number range for AT on the total AT volume was investigated.
RESULTS: The lower limits for CT number for pure subcutaneous AT, retroperitoneal AT, and visceral AT were -140, -140, and -130 HU, respectively. The corresponding upper limits were -70, -71, and -52 HU. The CT number range for AT using three methods when considering partial volume averaging was -144 to -141 HU to -30 to -33 HU, showing similar values between the three methods. The optimal CT number range for AT based on these data was -140 to -30 HU. Increases in total AT volume of 7.5 % and 1.8 % were found when the upper or lower limit was extended using 10 HU intervals, respectively, compared with the reference range of -140 to -30 HU.
CONCLUSION: This study demonstrated that the optimal CT number range of AT that is applicable to modern PET/CT systems can be defined as -140 to -30 HU. The use of this CT number range of AT allowed lean body mass to be determined in whole-body F-18 FDG PET/CT studies.

Entities:  

Keywords:  Adipose tissue; Body composition; Lean body mass; PET/CT

Year:  2012        PMID: 24900077      PMCID: PMC4043057          DOI: 10.1007/s13139-012-0175-3

Source DB:  PubMed          Journal:  Nucl Med Mol Imaging        ISSN: 1869-3474


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Authors:  S Rössner; W J Bo; E Hiltbrandt; W Hinson; N Karstaedt; P Santago; W T Sobol; J R Crouse
Journal:  Int J Obes       Date:  1990-10

2.  The five-level model: a new approach to organizing body-composition research.

Authors:  Z M Wang; R N Pierson; S B Heymsfield
Journal:  Am J Clin Nutr       Date:  1992-07       Impact factor: 7.045

Review 3.  Development of methods for body composition studies.

Authors:  Sören Mattsson; Brian J Thomas
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Authors:  S Demura; S Sato
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Authors:  N Mitsiopoulos; R N Baumgartner; S B Heymsfield; W Lyons; D Gallagher; R Ross
Journal:  J Appl Physiol (1985)       Date:  1998-07

6.  Adipose tissue volume determinations in women by computed tomography: technical considerations.

Authors:  H Kvist; L Sjöström; U Tylén
Journal:  Int J Obes       Date:  1986

7.  A multicompartment body composition technique based on computerized tomography.

Authors:  B Chowdhury; L Sjöström; M Alpsten; J Kostanty; H Kvist; R Löfgren
Journal:  Int J Obes Relat Metab Disord       Date:  1994-04

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