| dc.description.abstract | Stroke is caused by stenosis, occlusion, or rupture of a blood vessel supplying the brain, and is a leading cause of adult disability and death in the United States. However, due to advancements in therapies to prevent stroke, lessen stroke risk factors, and prevent stroke recurrence, the majority of strokes may be avoidable as risk factors become better defined.
MRI-measured functional parameters have demonstrated applicability for assigning therapies and diagnosing patients with cerebrovascular conditions. However, functional parameters are not always considered in clinical settings. By improving the accessibility and interpretability of functional images, patient care could be improved greatly, and these limitations are addressed here. More specifically, developments in this work include novel MRI methods for measuring cerebral metabolism, as well as machine learning and analysis techniques to classify patients according to their risk of cerebrovascular incident and to better interpret functional imaging. This is pursued through three primary aims. First, machine-learning techniques were utilized to identify characteristic moyamoya physiology from functional hemodynamic images, and this information was used to classify patients by severity determined by clinical indicators of impairment. Second, pre-surgical posterior flow-territory cerebrovascular reactivity was demonstrated to be greater in moyamoya participants with better response to surgical revascularization at a one-year follow up. Third, three variants of a novel MRI pulse sequence called the asymmetric spin echo were developed and compared for oxygen saturation metrics.
In summary, this work contributes to methodologies for utilizing and interpreting MRI functional imaging techniques, especially as they are applied to cerebrovascular disease. | |
