| Popis: |
Surgical management for hydrocephalus is among the most common procedures performed by pediatric neurosurgeons. However, how to best predict postoperative outcomes is unknown. Neuroimaging studies could provide insight, though working with these images is non-trivial. This thesis aims to 1) evaluate registration and preprocessing methodologies to best prepare data for comparisons, and 2) assess the impact of postoperative lateral ventricle volume (LVV) as a predictor of white matter health in networks that are dysregulated in hydrocephalus patients. We found that skull-stripped, bias corrected images with the SyN algorithm produced most accurate registration. We also found large dysregulated white matter networks in patients, and postoperative LVV did not have a large impact in predicting these networks. Overall, these studies suggest an image processing pipeline for pathological pediatric images and adds to the knowledge surrounding both the impact of pediatric hydrocephalus on white matter networks and the association with postoperative LVV. |
