title: Validation of supervised automated algorithm for fast quantitative evaluation of organ motion on magnetic resonance imaging.
abstract: OBJECTIVE
  To validate a correlation coefficient template-matching algorithm applied to the supervised automated quantification of abdominal-pelvic organ motion captured on time-resolved magnetic resonance imaging.
  
  
  METHODS
  Magnetic resonance images of 21 patients across four anatomic sites were analyzed. Representative anatomic points of interest were chosen as surrogates for organ motion. The point of interest displacements across each image frame relative to baseline were quantified manually and through the use of a template-matching software tool, termed "Motiontrack." Automated and manually acquired displacement measures, as well as the standard deviation of intrafraction motion, were compared for each image frame and for each patient.
  
  
  RESULTS
  Discrepancies between the automated and manual displacements of > or =2 mm were uncommon, ranging in frequency of 0-9.7% (liver and prostate, respectively). The standard deviations of intrafraction motion measured with each method correlated highly (r = 0.99). Considerable interpatient variability in organ motion was demonstrated by a wide range of standard deviations in the liver (1.4-7.5 mm), uterus (1.1-8.4 mm), and prostate gland (0.8-2.7 mm). The automated algorithm performed successfully in all patients but 1 and substantially improved efficiency compared with manual quantification techniques (5 min vs. 60-90 min).
  
  
  CONCLUSIONS
  Supervised automated quantification of organ motion captured on magnetic resonance imaging using a correlation coefficient template-matching algorithm was efficient, accurate, and may play an important role in off-line adaptive approaches to intrafraction motion management.
authors.affiliation: Department of Radiation Oncology, Princess Margaret Hospital, Toronto, ON, Canada.
authors.forename: Varuna
authors.initials: V
authors.lastname: Prakash
authors.forename: Jeffrey A
authors.initials: JA
authors.lastname: Stainsby
authors.forename: Janakan
authors.initials: J
authors.lastname: Satkunasingham
authors.forename: Tim
authors.initials: T
authors.lastname: Craig
authors.forename: Charles
authors.initials: C
authors.lastname: Catton
authors.forename: Philip
authors.initials: P
authors.lastname: Chan
authors.forename: Laura
authors.initials: L
authors.lastname: Dawson
authors.forename: Jennifer
authors.initials: J
authors.lastname: Hensel
authors.forename: David
authors.initials: D
authors.lastname: Jaffray
authors.forename: Michael
authors.initials: M
authors.lastname: Milosevic
authors.forename: Alan
authors.initials: A
authors.lastname: Nichol
authors.forename: Marshall S
authors.initials: MS
authors.lastname: Sussman
authors.forename: Gina
authors.initials: G
authors.lastname: Lockwood
authors.forename: Cynthia
authors.initials: C
authors.lastname: Ménard
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009068:Movement; D009369:Neoplasms / Q000473:pathology* / Q000532:radiotherapy*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011881:Radiotherapy, Computer-Assisted / Q000379:methods; D012680:Sensitivity and Specificity; D014781:Viscera / Q000473:pathology*