title: Comparison of acute and chronic traumatic brain injury using semi-automatic multimodal segmentation of MR volumes.
abstract: Although neuroimaging is essential for prompt and proper management of traumatic brain injury (TBI), there is a regrettable and acute lack of robust methods for the visualization and assessment of TBI pathophysiology, especially for of the purpose of improving clinical outcome metrics. Until now, the application of automatic segmentation algorithms to TBI in a clinical setting has remained an elusive goal because existing methods have, for the most part, been insufficiently robust to faithfully capture TBI-related changes in brain anatomy. This article introduces and illustrates the combined use of multimodal TBI segmentation and time point comparison using 3D Slicer, a widely-used software environment whose TBI data processing solutions are openly available. For three representative TBI cases, semi-automatic tissue classification and 3D model generation are performed to perform intra-patient time point comparison of TBI using multimodal volumetrics and clinical atrophy measures. Identification and quantitative assessment of extra- and intra-cortical bleeding, lesions, edema, and diffuse axonal injury are demonstrated. The proposed tools allow cross-correlation of multimodal metrics from structural imaging (e.g., structural volume, atrophy measurements) with clinical outcome variables and other potential factors predictive of recovery. In addition, the workflows described are suitable for TBI clinical practice and patient monitoring, particularly for assessing damage extent and for the measurement of neuroanatomical change over time. With knowledge of general location, extent, and degree of change, such metrics can be associated with clinical measures and subsequently used to suggest viable treatment options.
authors.affiliation: Laboratory of Neuro Imaging, University of California, Los Angeles, California 90095, USA. andrei.irimia@loni.ucla.edu
authors.forename: Andrei
authors.initials: A
authors.lastname: Irimia
authors.forename: Micah C
authors.initials: MC
authors.lastname: Chambers
authors.forename: Jeffry R
authors.initials: JR
authors.lastname: Alger
authors.forename: Maria
authors.initials: M
authors.lastname: Filippou
authors.forename: Marcel W
authors.initials: MW
authors.lastname: Prastawa
authors.forename: Bo
authors.initials: B
authors.lastname: Wang
authors.forename: David A
authors.initials: DA
authors.lastname: Hovda
authors.forename: Guido
authors.initials: G
authors.lastname: Gerig
authors.forename: Arthur W
authors.initials: AW
authors.lastname: Toga
authors.forename: Ron
authors.initials: R
authors.lastname: Kikinis
authors.forename: Paul M
authors.initials: PM
authors.lastname: Vespa
authors.forename: John D
authors.initials: JD
authors.lastname: Van Horn
mesh_terms: D000208:Acute Disease; D000328:Adult; D001930:Brain Injuries / Q000175:diagnosis* / Q000628:therapy; D002908:Chronic Disease; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged