Nearly 1.7 million Americans suffer traumatic brain injury (TBI) annually, which constitutes a significant US medical health concern. Although neuroimaging plays an important role in pathology localization and surgical planning, TBI clinical care does not currently take full advantage of neuroimaging computational technology. We propose to develop, validate, and commercialize computational algorithms, based on our methods for image segmentation and registration. These methods 1) can accommodate the presence of large pathologies in TBI cases, 2) can yield quantitative measures from chronic and acute TBI data for research into characterizing injury, monitoring pathology evolution, informing patient prognosis, and 3) can aid clinicians in optimizing TBI patient care workflows. We will accomplish our goal during the proposed Phase II effort by building upon our Phase I successes. Featured in conference and journal publications, during Phase I we devised a novel low-rank+sparse method for registering brain MRI scans from TBI patients with large pathologies to healthy brain atlases, enabling more accurate identification and quantification of anatomic changes. In conjunction with our foundational geometric metamorphosis work into quantifying lesion infiltration and recession over time, our set of methods now address the major hurdles associated with TBI patient understanding. Under this Phase II STTR proposal we will specifically focus on extending our computational methods for multimodal neuroimaging of TBI data processing. We will 1) provide finite element models created over a range of clinical cases of mild-to-severe TBI, 2) determine refined measures of patient change from longitudinal registrations, 3) integrate those methods into local and cloud-based environments that support academic and commercial use, and 4) validate the complete commercial system using extensive TBI data collections including neuropsychological motor, cognitive and behavioral outcome measures, in a customer- oriented study.

Public Health Relevance

In the US, approximately 1.7 million individuals are victims of traumatic brain injury (TBI) annually, with many requiring surgical intervention or long-term care. Initial assessment and treatment of TBI have appropriately become major US healthcare initiatives, yet the effects of TBI can be particularly challenging for the patient and for healthcre systems. Neuroimaging data analysis methods, however, are presently not properly employed to address this challenge. Herein we propose to refine, apply, and test tools initiated under our Phase I STTR to perform the combined, efficient analysis of multimodal neuroimage data sets for use in assessing the extent of brain injury, its change over time, and its effective treatment.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
Project #
2R44NS081792-03A1
Application #
8980724
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Fertig, Stephanie
Project Start
2013-01-01
Project End
2017-07-31
Budget Start
2015-09-30
Budget End
2016-07-31
Support Year
3
Fiscal Year
2015
Total Cost
Indirect Cost
Name
Kitware, Inc.
Department
Type
DUNS #
010926207
City
Clifton Park
State
NY
Country
United States
Zip Code
12065
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Irimia, Andrei; Van Horn, John D; Vespa, Paul M (2018) Cerebral microhemorrhages due to traumatic brain injury and their effects on the aging human brain. Neurobiol Aging 66:158-164
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Van Horn, John Darrell; Bhattrai, Avnish; Irimia, Andrei (2017) Multimodal Imaging of Neurometabolic Pathology due to Traumatic Brain Injury. Trends Neurosci 40:39-59
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Eickhoff, Simon; Nichols, Thomas E; Van Horn, John D et al. (2016) Sharing the wealth: Neuroimaging data repositories. Neuroimage 124:1065-8

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