## 4 Discussion
### Literature Analysis
#### 4.2.2 Description of Advanced Computerized Techniques for Image Processing Pre-Processing  
Pre-processing techniques involve the raw file that contains the patient dataset before it is processed to obtain interpretable multiplanar images. WBCT is a computerized tomography technique, meaning that the raw file is treated like a 3D stack of 2D slices. Unlike MDCT, in which it is the case because the acquisition requires'slicing' up the anatomy with a 'flat', 0.5-0.8 mm-thick fan-shaped X-ray beam, WBCT acquisition results in a single raw file considered isotropic (the image definition is the same in all dimensions of space), due to the cone-shaped beam. Pre-processing has the single raw file as input and outputs a stack of 2D 0.2-0.4 mm slices in the form of DICOM files. Other examples of pre-processing steps are metal artifact and movement reduction algorithms. They are based on highly specialized mathematical algorithms with the aim of improving the general quality of images and thus diagnostics. Post-Processing  
1. Pre-Segmentation  
2. Reconstruction  
The initial post-processing is performed by the WBCT device manufacturer's software ([[https://curvebeamai.com/products/cubevue-software/download-cubevue/](https://curvebeamai.com/products/cubevue-software/download-cubevue/)]([https://curvebeamai.com/products/cubevue-software/download-cubevue/](https://curvebeamai.com/products/cubevue-software/download-cubevue/)), accessed on 5 January 2024) to obtain interpretable multiplanar images, usually presented in multiplanar reconstruction format, with three viewing windows corresponding to the three planes of space (two vertical planes, coronal and sagittal; and one horizontal, axial plane) sometimes including a fourth window with a 3D volume rendering view (Figure 6). After this process, advanced post-processing techniques are subsequently applied to provide recent innovations. 3. Manual Measurements  
At this stage, classical measurements can be made manually, despite, as described earlier, inducing'slice bias'. Innovative solutions for manual measurements include the measurement of middle facet uncoverage in progressive collapsing foot deformities and  
Figure 6: Example of a full MPR rendering with coronal, sagittal and axial views and a 3D fourth window. the measurement of joint space width in ankle osteoarthritis using variations in contrast (measured in Hounsfield units (Hus)) [25; 45]. 4. Semi-Automatic 3D Biometrics  
The most reported post-processing, pre-segmentation semi-automatic tool is Cubeview Talas(r) (Curvebeam AI, Hatfield, PA, USA), which automatically gives the foot-ankle offset (FAO), a 3D biometric hindfoot alignment measurement, after manual identification of four anatomical landmarks: the weight-bearing points of M1 and M5, the calcaneus bones and the center of the ankle joint. Its result is given as a percentage offset of the foot length, which corresponds to the coronal offset between the center of the ankle joint and the bisector of the forefoot passing through the calcaneus weight-bearing point (Figure 7). The FAO has proven to be an effective hindfoot alignment measurement [48], and from it originated the concept of 3D biometrics, in which a minimum of four points are required to obtain volumetric (3D) rather than surface, angular or linear measurements. Its intra- and interobserver reliability is improved compared to conventional 2D measurements [47; 48] by levels nearing 100%, and its correlation with pathologies such as chronic lateral ankle instability [47] and PCFD has proven excellent. This is thought to be because the FAO takes into account the torsional effect of the forefoot on the hindfoot, unlike traditional measurements which only look at the alignment of the tibia versus the hindfoot. It may therefore be more sensitive in its ability to correlate with specific multidimensional pathologies like predicting the need for realignment osteetomies in total ankle replacement [81] or the risk of periprosthetic cysts [82].