Quantitative magnetic resonance imaging in a naturally occurring canine model of spinal cord injury | Academic Article individual record
abstract

STUDY DESIGN: Retrospective cohort study. OBJECTIVES: 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. SETTING: 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. CONCLUSION: 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.

author list (cited authors)
Griffin, J. F., Davis, M. C., Ji, J. X., Cohen, N. D., Young, B. D., & Levine, J. M.
publication date
2015
published in
SPINAL CORD Journal
keywords
  • Female
  • Dogs
  • Image Processing, Computer-Assisted
  • Algorithms
  • Spinal Cord Injuries
  • Magnetic Resonance Imaging
  • Animals
  • Pattern Recognition, Automated
  • Thoracic Vertebrae
  • Intervertebral Disc Displacement
  • Male
  • Retrospective Studies
  • Disease Models, Animal