title: Quantitative magnetic resonance imaging in a naturally occurring canine model of spinal cord injury.
abstract: METHODS
  Retrospective cohort study.
  
  
  OBJECTIVE
  To analyze magnetic resonance imaging (MRI) evaluator agreement in dogs with spinal cord injury (SCI) caused by intervertebral disk herniation (IVDH) using semiautomated and manual lesion segmentation and to analyze the associations between MRI and functional outcome.
  
  
  METHODS
  United States of America.
  
  
  METHODS
  T2-weighted MRIs from dogs with SCI resulting from thoracolumbar IVDH were identified from a database. Evaluators categorized MRIs on the basis of the presence or absence of a T2-hyperintense spinal cord lesion in axial and sagittal images. A semiautomated segmentation algorithm was developed and used to estimate the lesion volume. Agreement between evaluators and between semiautomated and manual segmentation was analyzed. The relationships of qualitative and quantitative MRIs with behavioral functional outcome were analyzed.
  
  
  RESULTS
  Axial images more commonly depicted lesions compared with sagittal images. Lesions in axial images had more consistent associations with functional outcome compared with sagittal images. There was imperfect qualitative agreement, and lesion volume estimation was imprecise. However, there was improved precision using semiautomated segmentation compared with manual segmentation.
  
  
  CONCLUSIONS
  Lesion volume estimation in dogs with naturally occurring SCI caused by IVDH is challenging, and axial images have important advantages compared with sagittal images. The semiautomated segmentation algorithm described herein shows promise but may require further refinement.
authors.affiliation: Department of Veterinary Large Animal Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA.
authors.forename: J F
authors.initials: JF
authors.lastname: Griffin
authors.affiliation: Department of Veterinary Large Animal Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA.
authors.forename: M C
authors.initials: MC
authors.lastname: Davis
authors.affiliation: Department of Electrical Engineering, Texas A&M University, College Station, TX, USA.
authors.forename: J X
authors.initials: JX
authors.lastname: Ji
authors.affiliation: Department of Veterinary Large Animal Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA.
authors.forename: N D
authors.initials: ND
authors.lastname: Cohen
authors.affiliation: Department of Veterinary Large Animal Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA.
authors.forename: B D
authors.initials: BD
authors.lastname: Young
authors.affiliation: Department of Veterinary Small Animal Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA.
authors.forename: J M
authors.initials: JM
authors.lastname: Levine
mesh_terms: D000465:Algorithms; D000818:Animals; D004195:Disease Models, Animal; D004285:Dogs; D005260:Female; D007091:Image Processing, Computer-Assisted; D007405:Intervertebral Disc Displacement / Q000150:complications / Q000473:pathology; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated; D012189:Retrospective Studies; D013119:Spinal Cord Injuries / Q000209:etiology / Q000473:pathology*; D013904:Thoracic Vertebrae