## 4 Materials and Methods  
Several stages are necessary to develop a high-fidelity numerical model of the human head, from medical imaging to geometry segmentation, ending with the development of the FE model and its validation, as seen in the literature [72; 73; 74; 75; 76]. Depending on the degree of complexity and desired accuracy, this methodology can go from an almost straightforward task to an extremely time-consuming work, e.g., when aiming for the detailed modelling of the central nervous system (CNS) structures. In this section, the authors present their numerical model of a young child's head. Its development was carried out in five well-defined stages (Figure 3): \begin{tabular}{l l l l l} \hline \hline  & **Water (wt\%)** & **Ash (wt\%)** & **Lipid (wt\%)** & **Protein (wt\%)** \\ \hline Whole brain & 76.3–78.5 (77.4) & 1.4–2 (1.5) & 9–17 & 8–12 \\ \hline Grey matter & 83–86 & 1.5 & 5.3 & 8–12 \\ \hline White matter & 68–77 & 1.4 & 18 & 11–12 \\ \hline \hline \end{tabular} \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Tissue** & **Research Method** & **Mechanical Properties** & **Age** \\ \hline [PERSON], & Brain (temporal cortex) & Shear stress–relaxation & Instantaneous shear modulus: 304 Pa & 5 years \\ \hline [PERSON] et al., 1983; & & & Axisymmetric tension & Stiffness: \\ [PERSON] et al., 1986 [69; 70] & & & & \\  & & & & \\ \hline  & & & Stiffness: & \\  & & & & 235 \(\pm\) 199 N/m & \\ [PERSON], 1991 [71] & Bridging veins & Uniaxial tension & Ultimate stress: & \\  & & & 12.02 \(\pm\) 5.9 MPa & 3–9 years \\  & & & Stretch ratio: & \\  & & & 1.67 \(\pm\) 0.27 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mechanical properties of brain tissues from paediatric heads. The first stage consists of gathering medical data and extracting the regions of interest for geometry generation. For a model to fit a wider population, it needs to respect the mean craniofacial anthropometric values. Therefore, in this work, 12 medical imaging sets from healthy individuals were considered and their craniometry assessed, selecting the case with a higher degree of similarity with the regional mean values. The selected dataset also included the contrast series and the angiographic CT series. This series includes at least 2/3 of the head within the scan, which would help recognize cerebral vessels. The main structures were segmented, and their geometry extracted. It is very important to add that the chosen DICOM (Digital Imaging and Communications in Medicine) set was based only on the MRI (Magnetic Resonance Imaging). Unlike for adults, where CTs are a common clinical approach, the MRI is a preferable medical approach for young children. However, extracting bone structures such as a skull from an MRI is a much more time-consuming procedure compared to the CT-based extraction or a combined MRI-CT approach. At this point, it should be noted that several years of intensive research have not been successful in establishing a generally accepted and undoubtedly  
Figure 3: Development of a numerical model of a small child’s head depicted in five stages. functioning segmentation algorithm that could automatically perform an MRI-based task for the brain instances. Therefore, mainly manual methods, as preferred in practice, are still used [77]. In stage II, with the aid of CAD (computer-aided design) tools, CATIA and Meshmixer software, 3D geometries were developed for the skull and brain, including their structural segmentation (e.g., white versus grey matter). These 3D geometries were then imported to a FE software to model the referred head structures (stage III). In stage III, a mesh-sensitive study is carried out, varying the type of element and their size. Triangular and tetrahedral elements, commonly used in FE head models, are associated with poor performance issues, such as the constant-strain triangle [78; 79], which is an issue in head modelling due to the existence of strain gradients.