Neuroimaging holds much promise for identifying markers of tissue salvageability after acute stroke and is therefore an ideal tool for accelerating assessment of new therapies. A limiting factor in the use of imaging for multi-center trials is that these techniques remain primarily qualitative, which complicates the aggregation of data across centers due to differing imaging protocols and platforms. The goal of our project is to develop quantitative imaging-based tools that can both be used with greater reliability within a single center and with greater reproducibility across multiple centers. Our proposal focuses on improving identification of tissue that can be salvaged by (1) reducing confounding influences of non-physiological factors and (2) improving objective monitoring of physiological changes in both natural history patients and patients receiving interventional therapies. Previously, we have shown that combining multiple acute MRI methods for measuring tissue and perfusion status can objectively and more accurately predict tissue infarction in both human and experimental stroke models than any single imaging modality. These algorithms have important clinical implications for reducing the sample size traditionally needed in stroke trials to demonstrate a therapeutic effect. The following studies for investigating cross-platform and cross-center reproducibility will therefore be performed to validate applicability of these algorithms for prospectively predicting tissue outcome: (1) analysis of retrospective acute MRI data sets from multiple stroke centers to identify sources of variability in imaging findings across hospitals and across platforms, and (2) analysis of prospectively collected imaging data to evaluate these algorithms'ability to evaluate the efficacy of new stroke therapies.
Stroke is a leading cause of death and morbidity in the US. This study develops tools for predicting the fate of tissue in stroke patients in response to different treatment strategies prior to therapy administration. This quantitative assessment of patient's infarction risk can be used for the objective evaluation of efficacy of novel therapies, thereby speeding the development of much-needed new stroke therapies.
|(2017) 19th Workshop of the International Stroke Genetics Consortium, April 28-29, 2016, Boston, Massachusetts, USA: 2016.001 MRI-defined cerebrovascular genomics-The CHARGE consortium. Neurol Genet 3:S2-S11|
|Edlow, Brian L; Copen, William A; Izzy, Saef et al. (2016) Diffusion tensor imaging in acute-to-subacute traumatic brain injury: a longitudinal analysis. BMC Neurol 16:2|
|Edlow, Brian L; Copen, William A; Izzy, Saef et al. (2016) Longitudinal Diffusion Tensor Imaging Detects Recovery of Fractional Anisotropy Within Traumatic Axonal Injury Lesions. Neurocrit Care 24:342-52|
|Bouts, Mark J R J; Westmoreland, Susan V; de Crespigny, Alex J et al. (2015) Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates. BMC Neurosci 16:91|
|Copen, William A; Morais, Livia T; Wu, Ona et al. (2015) In Acute Stroke, Can CT Perfusion-Derived Cerebral Blood Volume Maps Substitute for Diffusion-Weighted Imaging in Identifying the Ischemic Core? PLoS One 10:e0133566|
|Copen, W A; Deipolyi, A R; Schaefer, P W et al. (2015) Exposing hidden truncation-related errors in acute stroke perfusion imaging. AJNR Am J Neuroradiol 36:638-45|
|Wu, Ona; Cloonan, Lisa; Mocking, Steven J T et al. (2015) Role of Acute Lesion Topography in Initial Ischemic Stroke Severity and Long-Term Functional Outcomes. Stroke 46:2438-44|
|Jafari-Khouzani, Kourosh; Emblem, Kyrre E; Kalpathy-Cramer, Jayashree et al. (2015) Repeatability of Cerebral Perfusion Using Dynamic Susceptibility Contrast MRI in Glioblastoma Patients. Transl Oncol 8:137-46|
|Schaefer, P W; Pulli, B; Copen, W A et al. (2015) Combining MRI with NIHSS thresholds to predict outcome in acute ischemic stroke: value for patient selection. AJNR Am J Neuroradiol 36:259-64|
|Battey, Thomas W K; Karki, Mahima; Singhal, Aneesh B et al. (2014) Brain edema predicts outcome after nonlacunar ischemic stroke. Stroke 45:3643-8|
Showing the most recent 10 out of 23 publications