Nearly 1.7 million Americans suffer traumatic brain injury (TBI) annually, which constitutes an important and 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 and validate computational algorithms, based on image segmentation, registration and analysis, which yield quantitative measures to characterize injury, monitor pathology evolution, inform patient prognosis and optimize patient care workflows. This project addresses the current clinical need for informative TBI metrics and the technical need for easy-to-use image analysis tools capable of handling large, heterogenous pathologies that cause severe brain deformations.
In Aim 1, we will perform multimodal brain image segmentation for the assessment of acute and chronic TBI, and for measuring longitudinal changes. We will generate quantitative measures of TBI pathology that are based on segmenting lesions, hemorrhages, ventricles, gray matter (GM), white matter (WM) and the brain midline from multimodal image datasets. Clinically, these metrics will be used to quantitatively describe and assess injury at any time point (acute, chronic) and for longitudinal tracking based on pathology type, location and extent.
The second aim of this project is to advance the state-of-the-art in image registration for acute and chronic assessment of TBI and for longitudinal change measurement. Deformable image registration aligns corresponding anatomy in images and returns a displacement or flow field encapsulating the deformations between them. We will continue development of """"""""geometric metamorphosis"""""""", can register images with significant appearance changes caused by structures that grow or contract, such as TBI pathologies. We will derive novel voxel-wise quantifications and visualizations of pathology infiltration and of brain deformations induced by injury or longitudinal brain changes, both within lesions and within GM and WM.
The third aim i s to investigate the ability of our novel TBI metrics, derived from image segmentation and registration, to predict outcome and guide clinical decision making. The focus is on final clinical impact and on evaluating the relationship between brain remodeling (e.g. structural changes) with functional recovery or decline. We will use multivariate statistical methods to evaluate the prognostic abilities of the novel multimodal image-based measures of TBI (volumetric and deformation-based) from Aims 1-2 with respect to the neuropsychological motor, cognitive and behavioral outcome measures available for each TBI patient. Multivariate techniques will also allow investigation into the grouping of patient sub populations based on statistical features that describe their commonalities or optimally differentiate between them. This will aid in the customization of clinical workflows specific to each patient sub- group. Ultimately, the technical advances being proposed here will yield the ability to use imaging to monitor brain responses to trauma in an integrative, longitudinal fashion, with maximal clinical utility and specificity.
This study aims to develop and validate computational image analysis methods that yield quantitative measures to characterize injury, monitor pathology evolution, inform patient prognosis and optimize patient care workflows for traumatic brain injury (TBI) patients. This project addresses the current public health need for informative TBI measures and the technical need for image analysis tools that handle large, heterogenous pathologies that cause large brain deformations. Ultimately, the technical advances being proposed may yield an improved clinical ability to monitor brain responses to trauma in an integrative, longitudinal fashion.
|Irimia, Andrei; Goh, Sheng-Yang Matthew; Wade, Adam C et al. (2017) Traumatic Brain Injury Severity, Neuropathophysiology, and Clinical Outcome: Insights from Multimodal Neuroimaging. Front Neurol 8:530|
|Han, Xu; Yang, Xiao; Aylward, Stephen et al. (2017) EFFICIENT REGISTRATION OF PATHOLOGICAL IMAGES: A JOINT PCA/IMAGE-RECONSTRUCTION APPROACH. Proc IEEE Int Symp Biomed Imaging 2017:10-14|
|Goh, S Y Matthew; Irimia, Andrei; Torgerson, Carinna M et al. (2015) Longitudinal quantification and visualization of intracerebral haemorrhage using multimodal magnetic resonance and diffusion tensor imaging. Brain Inj 29:438-45|
|Liu, Xiaoxiao; Niethammer, Marc; Kwitt, Roland et al. (2015) Low-Rank Atlas Image Analyses in the Presence of Pathologies. IEEE Trans Med Imaging 34:2583-91|
|Irimia, Andrei; Torgerson, Carinna M; Goh, S-Y Matthew et al. (2015) Statistical estimation of physiological brain age as a descriptor of senescence rate during adulthood. Brain Imaging Behav 9:678-89|
|Irimia, Andrei; Van Horn, John Darrell (2015) Epileptogenic focus localization in treatment-resistant post-traumatic epilepsy. J Clin Neurosci 22:627-31|
|Irimia, A; Labus, J S; Torgerson, C M et al. (2015) Altered viscerotopic cortical innervation in patients with irritable bowel syndrome. Neurogastroenterol Motil 27:1075-81|
|Liu, Xiaoxiao; Niethammer, Marc; Kwitt, Roland et al. (2014) Low-rank to the rescue - atlas-based analyses in the presence of pathologies. Med Image Comput Comput Assist Interv 17:97-104|
|Irimia, A; Goh, S Y; Torgerson, C M et al. (2014) Structural and connectomic neuroimaging for the personalized study of longitudinal alterations in cortical shape, thickness and connectivity after traumatic brain injury. J Neurosurg Sci 58:129-44|
|Frohlich, Joel; Van Horn, John D (2014) Reviewing the ketamine model for schizophrenia. J Psychopharmacol 28:287-302|
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