title: Automated scan prescription for MR imaging of deformed and normal livers.
abstract: OBJECTIVE
  We propose an automated scan prescription to assess normal and deformed livers and demonstrate its efficacy in normal volunteers and in simulated deformed livers.
  
  
  METHODS
  Our automated scan prescription can be used to identify the upper and lower edges of the liver enables in commonly used axial slice positioning. The liver's upper edge is detected by template matching and finally identified by applying an active shape model to a sagittal projection image. The lower edge is detected using a maximum a posteriori (MAP) probability estimate that utilizes statistical information from a region of interest (ROI) placed in the liver. This places no restraints on liver shape and is therefore effective in assessing a deformed liver. Following institutional review and approval, we tested our method in 45 healthy volunteers. We also used clinical information to simulate deformed livers and tested our method with those datasets offline.
  
  
  RESULTS
  We could detect the upper edges within an error range of -3 to 6 mm, even without intensity correction for normal volunteers. Similar detection of the lower edges with maximum 21-mm and 7.84-mm standard deviation for normal volunteers confirmed the superior efficacy of our modified approach for deformed livers to that using our previous method. Clinical use required approximately 10 s' computational time on a Core i5 laptop with 2-GB memory.
  
  
  CONCLUSIONS
  We propose a method for automated scan prescription in magnetic resonance (MR) imaging of the liver and demonstrate the efficacy of our algorithm for evaluating deformed livers within a practical computation time. Detection of liver edges of various shapes by applying the MAP estimate combined with statistical information from the ROI demonstrated the potential clinical utility of this technique.
authors.affiliation: GE Healthcare Japan, MR Laboratory, Tokyo, Japan. Takao.Goto@ge.com
authors.forename: Takao
authors.initials: T
authors.lastname: Goto
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Kabasawa
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008099:Liver / Q000473:pathology*; D008107:Liver Diseases / Q000473:pathology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity