{"id":{"repo_id":"uic","oai_identifier":"oai:figshare.com:article/32995241"},"canonical_url":"https://search.dev.ndltd.org/etd/uic/oai:figshare.com:article/32995241","repository":{"repo_id":"uic","name":"University of Illinois - Chicago","base_url":"https://api.figshare.com/v2/oai"},"display":{"title":"Integrating Serial Quantitative Imaging with Clinical Data to Predict Brain Hemorrhage Outcomes","abstract":"Serial brain imaging, particularly non-contrast computed tomography (NCCT), is routinely used to diagnose, monitor, and inform clinical decisions in patients with brain hemorrhage. Subarachnoid hemorrhage (SAH) is a devastating form of brain hemorrhage in which blood erupts into the space surrounding the brain, potentially leading to severe neurologic complications, such as stroke or seizures, or even death. The link between SAH and the development of poor long-term outcomes, including recurrent seizures (i.e. epilepsy), is not fully understood. NCCT can be combined with other clinical data to help elucidate the link between SAH and the development of epilepsy, as well as other poor outcomes. However, computational approaches to realize this are limited. In this thesis, we developed approaches to extract quantitative measures from serial NCCT images and relate these to other clinical, laboratory, and electroencephalography (EEG) measures, to improve our understanding of and ability to predict patient outcomes after SAH.","abstract_html":"Serial brain imaging, particularly non-contrast computed tomography (NCCT), is routinely used to diagnose, monitor, and inform clinical decisions in patients with brain hemorrhage. Subarachnoid hemorrhage (SAH) is a devastating form of brain hemorrhage in which blood erupts into the space surrounding the brain, potentially leading to severe neurologic complications, such as stroke or seizures, or even death. The link between SAH and the development of poor long-term outcomes, including recurrent seizures (i.e. epilepsy), is not fully understood. NCCT can be combined with other clinical data to help elucidate the link between SAH and the development of epilepsy, as well as other poor outcomes. However, computational approaches to realize this are limited. In this thesis, we developed approaches to extract quantitative measures from serial NCCT images and relate these to other clinical, laboratory, and electroencephalography (EEG) measures, to improve our understanding of and ability to predict patient outcomes after SAH.","abstract_has_math":false,"creators":["Mitchell Butler (12241259)"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-05-01T00:00:00Z","date_published":"2026-05-01T00:00:00Z","updated_at":"2026-07-27T21:33:52Z","subjects":["Engineering, Biomedical","Vascular Neurology"],"languages":[],"rights":["In Copyright","Open Access after 2028-05-01"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.25417/uic.32995241.v1","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Mitchell Butler (12241259)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2026-05-01T00:00:00Z"]},{"key":"dc:relation","label":"Dc Relation","values":["https://figshare.com/articles/thesis/Integrating_Serial_Quantitative_Imaging_with_Clinical_Data_to_Predict_Brain_Hemorrhage_Outcomes/32995241"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering, Biomedical","Vascular Neurology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright","Open Access after 2028-05-01"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["10.25417/uic.32995241.v1"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Serial brain imaging, particularly non-contrast computed tomography (NCCT), is routinely used to diagnose, monitor, and inform clinical decisions in patients with brain hemorrhage. Subarachnoid hemorrhage (SAH) is a devastating form of brain hemorrhage in which blood erupts into the space surrounding the brain, potentially leading to severe neurologic complications, such as stroke or seizures, or even death. The link between SAH and the development of poor long-term outcomes, including recurrent seizures (i.e. epilepsy), is not fully understood. NCCT can be combined with other clinical data to help elucidate the link between SAH and the development of epilepsy, as well as other poor outcomes. However, computational approaches to realize this are limited. In this thesis, we developed approaches to extract quantitative measures from serial NCCT images and relate these to other clinical, laboratory, and electroencephalography (EEG) measures, to improve our understanding of and ability to predict patient outcomes after SAH."]},{"key":"dc:title","label":"Title","values":["Integrating Serial Quantitative Imaging with Clinical Data to Predict Brain Hemorrhage Outcomes"]}]}],"canonical_facts":{"dc:creator":["Mitchell Butler (12241259)"],"dc:date":["2026-05-01T00:00:00Z"],"dc:description":["Serial brain imaging, particularly non-contrast computed tomography (NCCT), is routinely used to diagnose, monitor, and inform clinical decisions in patients with brain hemorrhage. Subarachnoid hemorrhage (SAH) is a devastating form of brain hemorrhage in which blood erupts into the space surrounding the brain, potentially leading to severe neurologic complications, such as stroke or seizures, or even death. The link between SAH and the development of poor long-term outcomes, including recurrent seizures (i.e. epilepsy), is not fully understood. NCCT can be combined with other clinical data to help elucidate the link between SAH and the development of epilepsy, as well as other poor outcomes. However, computational approaches to realize this are limited. In this thesis, we developed approaches to extract quantitative measures from serial NCCT images and relate these to other clinical, laboratory, and electroencephalography (EEG) measures, to improve our understanding of and ability to predict patient outcomes after SAH."],"dc:identifier":["10.25417/uic.32995241.v1"],"dc:relation":["https://figshare.com/articles/thesis/Integrating_Serial_Quantitative_Imaging_with_Clinical_Data_to_Predict_Brain_Hemorrhage_Outcomes/32995241"],"dc:rights":["In Copyright","Open Access after 2028-05-01"],"dc:subject":["Engineering, Biomedical","Vascular Neurology"],"dc:title":["Integrating Serial Quantitative Imaging with Clinical Data to Predict Brain Hemorrhage Outcomes"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T21:33:52Z"}