Automatic correction of intensity inhomogeneities improves unsupervised assessment of abdominal fat by MRI

Vincenzo Positano, Kenneth Cusi, Maria Filomena Santarelli, Annamaria Sironi, Roberta Petz, Ralph DeFronzo, Luigi Landini, Amalia Gastaldelli

Research output: Contribution to journalArticle

23 Scopus citations

Abstract

Purpose: To demonstrate that unsupervised assessment of abdominal adipose tissue distribution by magnetic resonance imaging (MRI) can be improved by integrating automatic correction of signal inhomogeneities. Materials and Methods: Twenty subjects (body mass index [IBMI] 23.7-44.0 kg/m2) underwent abdominal (32 slices) MR imaging with a 1.9T Elscint Prestige scanner. Many images were affected by relevant intensity distortions. Unsupervised segmentation of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) was performed by a previously validated algorithm exploiting standard fuzzy clustering segmentation. Images were also processed by an improved version of the software, including automatic correction of intensity inhomogeneities. To assess the effectiveness of the two methods SAT and VAT volumes were compared with manual analysis performed by a trained operator. Results: Coefficient of variation between manual and unsupervised analysis was significantly improved by inhomogeneities correction in SAT evaluation. Systematic underestimation of SAT was also corrected. A less important performance improvement was found in VAT measurement. Conclusion: The results of this study suggest that the compensation of signal inhomogeneities greatly improves the effectiveness of the unsupervised assessment of abdominal fat. Correction of intensity distortions is important in SAT evaluation and less significant in VAT measurement.

Original languageEnglish (US)
Pages (from-to)403-410
Number of pages8
JournalJournal of Magnetic Resonance Imaging
Volume28
Issue number2
DOIs
StatePublished - Aug 1 2008

Keywords

  • Abdominal fat
  • Fuzzy clustering
  • Image processing
  • MRI

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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