# DTI Histogram and Texture Features as Early Predictors of Post-Radiotherapy Cognitive Decline
## 2 Materials and Methods
### Image Processing and Region of Interest Definition  
A semi-automated image co-registration process aligned maps of DTI indices and images of conventional MRI sequences to the baseline dataset using Statistical Parametric Mapping (SPM12, Functional Imaging Laboratory, UCL Queen Square Institute of Neurology, London, UK). All images were spatially normalized to the Montreal Neurological Institute (MNI) standard space using a cost-function masking approach, where lesions were masked to prevent distortion during the normalization process. The spatial normalization parameters included affine regularization optimized for East Asian brains, with 4 th-degree B-spline interpolation to maintain image quality. Brain parenchyma (BP) was segmented from normalized FLAIR or echo planar images with no diffusion weighting (b\({}_{0}\)) images using a signal intensity threshold set-up  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & **TR/TE (ms)** & **Voxel Size (mm\({}^{3}\))** & **Slice Thickness (mm)** & **Interslice Gap (mm)** & **Number of (mm)** & **Acquisition** (mm)** & **FOOTI: Data Processing and Analysis of the MRI sequences. \begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{DTI} & \multirow{2}{*}{5100/139} & \multirow{2}{*}{0.9375 \(\times\) 0.9375 \(\times\) 5} & \multirow{2}{*}{5} & \multirow{2}{*}{1.5} & \multirow{2}{*}{23} & \multirow{2}{*}{2} & \multirow{2}{*}{256 \(\times\) 256} & \multirow{2}{*}{240 \(\times\) 240} & \multirow{2}{*}{12 motion-} \\  & & & & & & & & & & & \\ \hline \multirow{4}{*}{T1 WI} & \multirow{2}{*}{700/15} & \multirow{2}{*}{0.9375 \(\times\) 0.9375 \(\times\) 5} & \multirow{2}{*}{5} & \multirow{2}{*}{1.5} & \multirow{2}{*}{19} & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{256 \(\times\) 192} & \multirow{2}{*}{240 \(\times\)  
in MRICron (version 1.0.2, [PERSON]'s Neuropsychology Lab, University of South Carolina, Columbia, SC, USA, [[https://www.nitrc.org/projects/mricron](https://www.nitrc.org/projects/mricron)]([https://www.nitrc.org/projects/mricron](https://www.nitrc.org/projects/mricron)) (accessed on 5 January 2022)) [28]. To determine the appropriate segmentation thresholds, MRI signal intensities were measured across 14 normal-appearing brain regions, including the gray matter of the bilateral frontal, parietal, temporal, and occipital lobes, as well as the pons, leniform nucleus, and cerebellar hemispheres. The mean and standard deviation (SD) of the measured signal intensities were calculated for each time point. The normal-appearing BP (NABP) was then delineated using the formula: BP\({}_{\text{signal}}\)\(\leq\) Mean + (0.5 \(\times\) SD), where BP\({}_{\text{signal}}\) represents the calculated MRI signal intensity for BP. Here, NABP refers to regions that are structurally intact on conventional MRI but potentially exhibit microstructural abnormalities detectable with DTI. Subsequently, WM was segmented from T1-weighted images using SPM12's segmentation algorithm with affine regularization optimized for East Asian brains, and the deformation field was set forward. In this analysis, we focused on NAWM, defined as WM within NABP. An experienced neuroradiologist visually assessed registration accuracy and spatial normalization procedures to ensure accuracy. The complete image processing workflow from DTI map reconstruction to NAWM extraction is illustrated in Figure 1.  
Figure 1: Workflow for extracting diffusion tensor imaging (DTI) features from normal-appearing white matter (NAWM).