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.
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.
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| Han, Xu; Kwitt, Roland; Aylward, Stephen et al. (2018) Brain extraction from normal and pathological images: A joint PCA/Image-Reconstruction approach. Neuroimage 176:431-445 |
| 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 |
| Palacios, E M; Martin, A J; Boss, M A et al. (2017) Toward Precision and Reproducibility of Diffusion Tensor Imaging: A Multicenter Diffusion Phantom and Traveling Volunteer Study. AJNR Am J Neuroradiol 38:537-545 |
| Yang, Xiao; Kwitt, Roland; Styner, Martin et al. (2017) Quicksilver: Fast predictive image registration - A deep learning approach. Neuroimage 158:378-396 |
| Yang, Xiao; Han, Xu; Park, Eunbyung et al. (2016) Registration of Pathological Images. Simul Synth Med Imaging (2016) 9968:97-107 |
| Aylward, S R; McCormick, M; Kang, H J et al. (2016) ULTRASOUND SPECTROSCOPY. Proc IEEE Int Symp Biomed Imaging 2016:1013-1016 |
| 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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