## 4 Discussion
### Examination Method and Algorithm  
The examination method and the algorithm itself consisted of several steps and interfaces between different software tools (Figure 3). Every step was optimizable and potentially erroneous, so we will discuss the individual steps. First of all, the image acquisition on the VFs was user dependent, because the VFs were compressed manually. The manual compression, therefore the strain field, was difficult to reproduce [16]. The shear-wave elastography could reduce the user dependency, bringing up other  
Figure 6: Measurements of the VFs. The VFs were fixed in the measurement setup and examined with the Alpinion IO8-17 transducer placed on the shown position (Figure 1a). The resulting images were evaluated with the presented algorithm: starting with the estimation of the displacement field (**a**), calculating the strain (**b**), and finally, the elastic modulus (**c**). (**a**) Displacement field in the tissue. (**b**) Strain in the tissue. (**c**) The elastic modulus of the VF.  
technical challenges [52]. For example, the choice of the different imaging and stimulation frequency was challenging.  
Due to the measurement on ex vivo VFs, the elastic modulus was measured on relaxed muscles. For this reason, the presented results were only valid for muscles free from tension. Our approach makes anesthesia of the patient mandatory, which leads to relaxation of the muscles. Therefore, the measurement on muscles under tension is not possible. The transcutaneous approach was able to display the VFs under movement [53; 54].  
The measurement method and the algorithm were validated on rectangular gelatin phantoms with a central inclusion. In this case, the proposed method showed good results. The geometrical and elastic properties of these phantoms are different from the ones of the VFs. Therefore, the validation could be refined in two ways. On the one hand, the validation could be done on artificial VFs, or on the other hand on a series of measurements of porcine VFs. Our future goal was to validate it in both suggested ways.  
The image registration of the video sequence was very time consuming. Every frame took around 10-15 min to register. An optimization of the calculation time was sought. The calculation could be reduced by reducing the iteration number of the optimizer, which possibly reduced the registration precision. Further investigations on the minimum number of iterations could be beneficial. Additionally, the registration could be done on a GPU [37].  
The filter parameters, specifically the filter window and order of the polynomial, for the Savitzky-Golay filter of the displacement field are empirical values. The exact choice needs to be validated and optimized. Furthermore, the parameters (e.g., boundary conditions and Tikhonov regularization parameter) of the FEM solver were chosen manually. The boundary conditions of the FEM were an approximation of the real ones, and the measurement of the forces in the tissue was not done. To use this approach in non-lab settings, further research on the FEM estimation would be necessary. Especially, an exact integration of the displacement at the borders would be desirable. As a result of the approximated boundary conditions, the corners of the measured elastic modulus of the phantom (Figure 5c) appeared to be very high.  
As a result, the algorithm will be enhanced. Especially, the automatic choice of the mentioned parameters and a shorter calculation time would be required for clinical applications.