title: Automatic correction of intensity inhomogeneities improves unsupervised assessment of abdominal fat by MRI.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  Twenty subjects (body mass index [BMI] 23.7-44.0 kg/m(2)) 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Institute of Clinical Physiology, CNR, Pisa, Italy. positano@ifc.cnr.it
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Positano
authors.forename: Kenneth
authors.initials: K
authors.lastname: Cusi
authors.forename: Maria Filomena
authors.initials: MF
authors.lastname: Santarelli
authors.forename: Annamaria
authors.initials: A
authors.lastname: Sironi
authors.forename: Roberta
authors.initials: R
authors.lastname: Petz
authors.forename: Ralph
authors.initials: R
authors.lastname: Defronzo
authors.forename: Luigi
authors.initials: L
authors.lastname: Landini
authors.forename: Amalia
authors.initials: A
authors.lastname: Gastaldelli
mesh_terms: D050153:Abdominal Fat / Q000033:anatomy & histology*; D000465:Algorithms; D015992:Body Mass Index; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged