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Comparison of Acute and Chronic Traumatic Brain Injury using Semi-automatic Multimodal Segmentation of MR Volumes

1Laboratory of Neuro Imaging, University of California, Los Angeles, CA, USA.
2Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
3Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
4University of California, Los Angeles, Neurosurgery, Los Angeles, California, USA.
5Surgical Planning Laboratory, Department of Radiology, Harvard Medical School, Boston, MA, USA.
6Brain Injury Research Center, Departments of Neurosurgery and Neurology, University of California, Los Angeles, CA, USA.
Publication Date:
J Neurotrauma
Volume Number:
Issue Number:
J Neurotrauma. 2011 Nov;28(11):2287-306.
PubMed ID:
magnetic resonance imaging, segmentation, TBI, visualization
Appears in Collections:
U54 EB005149/EB/NIBIB NIH HHS/United States
P01 NS058489/NS/NINDS NIH HHS/United States
Generated Citation:
Irimia A., Chambers M.C., Alger J.R., Filippou M., Prastawa M., Wang B., Hovda D., Gerig G., Toga A.W., Kikinis R., Vespa P.M., Van Horn J.D. Comparison of Acute and Chronic Traumatic Brain Injury using Semi-automatic Multimodal Segmentation of MR Volumes. J Neurotrauma. 2011 Nov;28(11):2287-306. PMID: 21787171. PMCID: PMC3218448.
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Though neuroimaging is essential for prompt and proper management of TBI, there is 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 (structural volume, atrophy measurements, etc.) 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.

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