Mechanical model | Young modulus | Poisson's ratio | Force |
---|
Assumed
| 0.45 | 3000 | Fx = 1500 |
 |  |  |  |  | Fy = 1500 |
 |  |  |  |  | Fz = 1500 |
Estimated
| 0.45 ± 0.0056 | 3000 ± 175.6 | Fx = 1500 ± 90.8 |
 |  |  |  |  | Fy = 1500 ± 87.9 |
 |  |  |  |  | Fz = 1500 ± 93.2 |
Nonlinear model
|
C100
|
C010
|
C200
|
C020
|
g
1
+g
2
|
Force
|
Assumed
| 263 | 263 | 491 | 491 | 0.815 | Fx = -300 |
 |  |  |  |  |  | Fy = 300 |
 |  |  |  |  |  | Fz = 300 |
Estimated
| 263 ±4.0401 | 263 ±7.8404 | 491 ±14.4139 | 491 ±13.4578 | 0.815 ±0.0083 | Fx = -300 ± 9.1726 |
 |  |  |  |  |  | Fy = 300 ± 10.0278 |
 |  |  |  |  |  | Fz = 300 ± 8.7998 |
- We use sphere as a simple model of the brain. Specific parameters are assumed and the resulting deformations of specific points are used in the optimization process. Other points are used for testing of the optimization process. Then, the parameters are changed and the optimization process is repeated to estimate the model parameters again. The results illustrate that the brain deformation can be modeled by the proposed models and the proposed optimization method can accurately estimate the model parameters. For each model, we have done this process and have compared the accuracy of models using the matching errors of the points used in the optimization process and those that are not used. Note that the optimization process estimates the parameters of the nonlinear model more accurately (with less bias and variance) compared with the mechanical model.