title: Accurate automatic papillary muscle identification for quantitative left ventricle mass measurements in cardiac magnetic resonance imaging.
abstract: OBJECTIVE
  We sought to evaluate the automatic detection of the papillary muscle and to determine its influence on quantitative left ventricular (LV) mass assessment.
  
  
  METHODS
  Twenty-eight Yorkshire-Landrace swine and 10 volunteers underwent cardiac magnetic resonance imaging (CMR) of the left ventricle. The variability in measurements of LV papillary muscles traced automatically and manually were compared to intra- and interobserver variabilities. CMR-derived LV mass with the papillary muscle included or excluded from LV mass measurements was compared to true mass at autopsy of the Yorkshire-Landrace swine.
  
  
  RESULTS
  Automatic LV papillary muscle mass from all subjects correlated well with manually derived LV papillary muscle mass measurements (r = 0.84) with no significant bias between both measurements (mean difference +/- SD, 0.0 +/- 1.5 g; P = .98). The variability in results related to the contour detection method used was not statistically significant different compared to intra- and interobserver variabilities (P = .08 and P = .97, respectively). LV mass measurements including the papillary muscle showed significantly less underestimation (-10.6 +/- 7.1 g) with the lowest percentage variability (6%) compared to measurements excluding the papillary muscles (mean underestimation, -15.1 +/- 7.4 g percentage variability, 7%).
  
  
  CONCLUSIONS
  The automatic algorithm for detecting the papillary muscle was accurate with variabilities comparable to intra- and interobserver variabilities. LV mass is determined most accurately when the papillary muscles are included in the LV mass measurements. Taken together, these observations warrant the inclusion of automatic contour detection of papillary muscle mass in studies that involve the determination of LV mass.
authors.affiliation: Department of Cardiology, Thoraxcenter, Rotterdam, The Netherlands.
authors.forename: Sharon
authors.initials: S
authors.lastname: Kirschbaum
authors.forename: Jean-Paul
authors.initials: JP
authors.lastname: Aben
authors.forename: Timo
authors.initials: T
authors.lastname: Baks
authors.forename: Amber
authors.initials: A
authors.lastname: Moelker
authors.forename: Katerina
authors.initials: K
authors.lastname: Gruszczynska
authors.forename: Gabriel P
authors.initials: GP
authors.lastname: Krestin
authors.forename: Wim J
authors.initials: WJ
authors.lastname: van der Giessen
authors.forename: Dirk J
authors.initials: DJ
authors.lastname: Duncker
authors.forename: Pim J
authors.initials: PJ
authors.lastname: de Feyter
authors.forename: Robert-Jan M
authors.initials: RJ
authors.lastname: van Geuns
mesh_terms: D000328:Adult; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D005260:Female; D006352:Heart Ventricles / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009929:Organ Size; D010210:Papillary Muscles / Q000033:anatomy & histology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013552:Swine