Integrating Serial Quantitative Imaging with Clinical Data to Predict Brain Hemorrhage Outcomes

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.

Author dc:creator

• Mitchell Butler (12241259)

• Engineering, Biomedical
• Vascular Neurology

Statement dc:rights

• In Copyright
• Open Access after 2028-05-01

DOI dc:identifier

https://doi.org/10.25417/uic.32995241.v1

OAI identifier oai:identifier

oai:figshare.com:article/32995241