#### Challenges to Preprocessing of Multi-Site TBI MRI Data
OptiBET especially shows a decline in performance on T2-FLAIR MRIs, which is a specialized MRI sequence crucial for the detailed visualization and segmentation of TBI-induced brain lesions ([PERSON] et al., 2014; [PERSON] et al., 2019). Intensity normalization and intensity inhomogeneity correction are also two essential steps in the preprocessing of multi-site TBI MRI data. While intensity inhomogeneity correction and intensity normalization represent two distinct preprocessing techniques, for simplicity, they are often collectively referred to as \"intensity processing\" in later sections. Intensity inhomogeneity correction mitigates spatial variations in signal intensity that are not inherent to the anatomical structure but rather result from the variations in the MRI scanner's magnetic field, called the bias field ([PERSON] et al., 2014; [PERSON] et al., 2019). Conversely, intensity normalization aims to standardize the distribution of intensity values. Normalizing the intensity distribution across different images allows for inter-scan and inter-scanner comparisons ([PERSON] et al., 2014; [PERSON] et al., 2014). In raw MRI data, variations in intensity can be attributed both to tissue properties and to the MRI scanner settings used during acquisition. Due to this dependency on scanner settings, comparing intensities across MRIs is problematic, especially when different settings or scanners are involved ([PERSON] et al., 2014). Variability persists among all scans, even when using identical scan and scanner settings, making intensity normalization essential for meaningful comparisons across MRI scans ([PERSON] et al., 2014; [PERSON] et al., 2014). Intensity normalization and intensity inhomogeneity  
Figure 1: Examples of brain extractions. **(a)** through **(d)** are of the same T2-FLAIR MRI brain image slice. Image **(a)** is the brain image before any extraction processes, **(b)** depicts an accurately extracted brain image with clear boundaries and minimal extraneous tissue, **(c)** shows a poorly extracted brain image using BET with options B and f = 0.4, and **(d)** depicts a poorly extracted brain image using optiBET. Images **(c,d)** exhibit noticeable artifacts and incorrect boundaries, with **(d)** also showing substantial internal holes. See Section 2.6 for details on dice similarity coefficients (DSCs). correction are critical for the accuracy and consistency of MRI data analysis and, thus, are essential components of MRI preprocessing pipelines [26; 27]. Considering the limitations of contemporary automated techniques in performing brain extractions in the presence of TBI, the fundamental importance of accurate brain extraction to the validity of MRI analyses, and the necessity for intensity processing for multi-site MRI data sets, there is an urgent need for the development of more reliable and efficient automated brain extraction methods and a greater understanding of the interplay of different preprocessing steps in general to create an optimized MRI preprocessing pipeline for TBI data. To address these challenges, the current investigation examines the utility of N3 bias-field correction from [PERSON] et al. [30] and three different intensity normalization algorithms from [PERSON] et al. [31] in refining the precision and reliability of T1-weighted Magnetization-Prepared Rapid Gradient-Echo (T1-MPRAGE) and T2-weighted Fluid-Attenuated Inversion Recovery (T2-FLAIR) TBI MRI brain extractions with optiBIET and BET. This study offers several critical contributions to the field of neuroimaging preprocessing, specifically addressing challenges in the automated extraction of patient brains. Our primary objective is to develop and validate optimized preprocessing techniques for automated brain extraction in patients with moderate to severe TBI, addressing a critical need in the analysis of large-scale, multi-site TBI MRI data.