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Multimodality Image-Based Assessment System for Traumatic Brain Injury

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 2R44NS081792-03A1
Agency Tracking Number: R44NS081792
Amount: $1,268,512.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: 101
Solicitation Number: PA14-072
Timeline
Solicitation Year: 2014
Award Year: 2015
Award Start Date (Proposal Award Date): 2015-09-30
Award End Date (Contract End Date): 2019-07-31
Small Business Information
28 CORPORATE DR
Clifton Park, NY 12065-8688
United States
DUNS: 010926207
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 STEPHEN AYLWARD
 (919) 966-9695
 aylward@unc.edu
Business Contact
 WILLIAM SCHROEDER
Phone: (518) 331-1177
Email: will.schroeder@kitware.com
Research Institution
N/A
Abstract

DESCRIPTION provided by applicant Nearly 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 can accommodate the presence of large pathologies in TBI cases can yield quantitative measures from chronic and acute TBI data for research into characterizing injury monitoring pathology evolution informing patient prognosis and 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 andquot low rank sparseandquot 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 andquot geometric metamorphosisandquot 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 provide finite element models created over a range of clinical cases of mild to severe TBI determine refined measures of patient change from longitudinal registrations integrate those methods into local and cloud based environments that support academic and commercial use and 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 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

* Information listed above is at the time of submission. *

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