Cochlear implants (CIs) are considered standard treatment for patients who experience sensory-based hearing loss. Although these devices have been remarkably successful at restoring hearing, it is rare to achieve natural fidelity, and many patients experience poor outcomes. Previous studies have shown that outcomes can be improved when optimizing CI processor settings using an estimation of the CI's neural activation patterns found by detecting the distance between the CI electrodes and the nerves they stimulate in pre- and post-implantation CT images. We call this method Image-Guided CI Programming (IGCIP). More comprehensive electro-anatomical models (EAMs) might better estimate neural activation patterns than using a distance-based estimate, potentially leading to selecting further optimized CI settings. Our goal in this study is to investigate whether μCT-based EAMs can accurately estimate neural stimulation patterns. For this purpose, we have constructed EAMs of N=9 specimens. We analyzed the sensitivity of our model to design parameters such as field-of-view, resolution, and tissue resistivity. Our results show that our model is stable to parameter changes. To evaluate the utility of patient-specific modeling, we quantify the difference in estimated neural activation patterns across specimens for identically located electrodes. The average computed coefficient of variation (COV) across specimens is 0.186, suggesting patient-specific models are necessary and that the accuracy of a generic model would be insufficient. Our results suggest that development of in vivo patient-specific EAMs could lead to better methods for selecting CI settings, which would ultimately lead to better hearing outcomes with CIs.