title: Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water-fat MRI.
abstract: OBJECTIVE
  To develop and demonstrate a rapid whole-body magnetic resonance imaging (MRI) method for automatic quantification of total and regional skeletal muscle volume.
  
  
  METHODS
  The method was based on a multi-atlas segmentation of intensity corrected water-fat separated image volumes. Automatic lean muscle tissue segmentations were achieved by nonrigid registration of atlas datasets with 10 different manually segmented muscle groups. Ten subjects scanned at 1.5 T and 3.0 T were used as atlases, initial validation and optimization. Further validation used 11 subjects scanned at 3.0 T. The automated and manual segmentations were compared using intraclass correlation, true positive volume fractions, and delta volumes.
  
  
  RESULTS
  For the 1.5 T datasets, the intraclass correlation, true positive volume fractions (mean ± standard deviation, SD), and delta volumes (mean ± SD) were 0.99, 0.91 ± 0.02, -0.10 ± 0.70L (whole body), 0.99, 0.93 ± 0.02, 0.01 ± 0.07L (left anterior thigh), and 0.98, 0.80 ± 0.07, -0.08 ± 0.15L (left abdomen). The corresponding values at 3.0 T were 0.97, 0.92 ± 0.03, -0.17 ± 1.37L (whole body), 0.99, 0.93 ± 0.03, 0.03 ± 0.08L (left anterior thigh), and 0.89, 0.90 ± 0.04, -0.03 ± 0.42L (left abdomen). The validation datasets showed similar results.
  
  
  CONCLUSIONS
  The method accurately quantified the whole-body skeletal muscle volume and the volume of separate muscle groups independent of field strength and image resolution.
authors.affiliation: Department of Biomedical Engineering (IMT), Linköping University, Linköping, Sweden.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
authors.forename: Anette
authors.initials: A
authors.lastname: Karlsson
authors.affiliation: Advanced MR Analytics (AMRA) AB, Linköping, Sweden.
authors.forename: Johannes
authors.initials: J
authors.lastname: Rosander
authors.affiliation: Department of Biomedical Engineering (IMT), Linköping University, Linköping, Sweden.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
authors.forename: Thobias
authors.initials: T
authors.lastname: Romu
authors.affiliation: Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
authors.forename: Joakim
authors.initials: J
authors.lastname: Tallberg
authors.affiliation: Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.|Department of Radiation Physics and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
authors.forename: Anders
authors.initials: A
authors.lastname: Grönqvist
authors.affiliation: Department of Biomedical Engineering (IMT), Linköping University, Linköping, Sweden.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
authors.forename: Magnus
authors.initials: M
authors.lastname: Borga
authors.affiliation: Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.|Department of Medical and Health Sciences (IMH), Linköping University, Linköping, Sweden.
authors.forename: Olof
authors.initials: O
authors.lastname: Dahlqvist Leinhard
mesh_terms: D000273:Adipose Tissue / Q000033:anatomy & histology; D000328:Adult; D001834:Body Water; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D018482:Muscle, Skeletal / Q000033:anatomy & histology*; D051598:Whole Body Imaging
keywords: MRI; classification; multi-atlas segmentation; muscle volume; muscles; registration