## 4 Discussion
### Future Work  
This analysis sets the groundwork for many potential future investigations. Our results suggest that N3 intensity inhomogeneity correction can significantly influence the accuracy and reliability of brain extraction outcomes; however, these effects are highly dependent on the specific procedure of N3 implemented, the MRI sequence, and the brain extraction configuration. While this exploration limited N3 to one and four iterations, it revealed a complex relationship between intensity inhomogeneity correction and brain extraction. This relationship is particularly pronounced in the contrast in outcomes between T1-MPRAGE and T2-FLAIR MRIs and BET configurations. Given this variability, future work should delve into a more granular investigation of intensity inhomogeneity correction beyond the current scope. This could include expanded testing of different N3 iterations using an initial brain mask with N3 (like those required for WS and KDE normalization) or performing intensity normalization after intensity inhomogeneity correction and vice versa. These approaches could enhance the uniformity and contrast of MRI images, potentially improving the accuracy of the subsequent brain extraction. Furthermore, BET | B,f0.1 has long been accepted as the best-performing BET configuration for patient data, such that BET | B,f0.1 is the configuration for the initial extraction in the optiBET script [20]. However, our results suggest that other configurations, namely, BET | R and BET | B,f0.2,g0.3, are better suited for TBI data. This could warrant a more systematic investigation into the optimal BET parameters for TBI data and how incorporating these different configurations into the optiBET script affects brain extraction. Altogether, the current and proposed investigations contribute to the creation of TBI-optimized brain extraction methods and, ultimately, TBI-optimized MRI preprocessing pipelines, ultimately enhancing the accuracy and efficiency of TBI MRI data analysis. This study has yielded new insights into the interplay between intensity normalization, inhomogeneity correction, and brain extraction. Our findings offer TBI researchers critical guidance for constructing MRI preprocessing pipelines that are better equipped to handle the complex and challenging nature of TBI data. By understanding these interactions, researchers can enhance the accuracy and reliability of brain extraction outcomes, ultimately improving the quality of data available for further TBI-related analyses. Conceptualization, P.C. ; methodology, P.C. ; software, P.C. ; validation, P.C., C.A., A.B. and D.D. ; formal analysis, P.C. ; investigation, P.C. ; resources, D.D. ; data curation, P.C., C.A. and K.K. ; writing--original draft preparation, P.C. ; writing--review and editing, P.C., C.A., A.B., K.K. and D.D. ; visualization, P.C. ; supervision, C.A., A.B. and D.D. ; project administration, P.C. and D.D. ; funding acquisition, D.D. All authors have read and agreed to the published version of the manuscript. This study was conducted with the support of the National Institute of Neurological Disorders and Stroke (NINDS) of the National Institutes of Health (NIH) under award numbers U54 NS100064 (EpiBio54 Rx) and R01 NS111744. The studies involving human participants were reviewed and approved by the UCLA Institutional Review Board (IRB# 16-001 576) and the local review boards at each EpiBio54 Rx Study Group Institution. Written informed consent to participate in this study was provided by the participant's legal guardian/next of kin. The datasets presented in this article are not readily available because the data described in this study are subject to the following licenses/restrictions: access to data must be requested and approved by the EpiBio54 Rx Steering Committee. Requests to access the datasets should be directed to [EMAIL_ADDRESS].