Research on the Contour Modeling Method of Peripheral Nerve Internal Fascicular Groups During the Non-Splitting/Merging Phase and Distribution Rules of Model Parameters

Objectives: To investigate benchmark data for docking the same functional nerve bundles based on the mathematical contour model of peripheral nerve internal fascicular groups.

Materials and Methods: First, the discrete points of the original contours of nerve bundles were extracted into a dataset through the image process. Second, two indicators were employed to evaluate the modeling precision. Third, the dataset was modeled by the 3rd-order quasi-uniform B-spline method. Fourth, the dataset was modeled by the Fourier transform method. Fifth, all contours were modeled by the 4th-order Fourier method. Then, the histogram of each parameter from the Fourier model was calculated. Furthermore, the probability density function was fit to each parameter.

Results: First, the optimized sampling number of the 3rd-order quasi-uniform B-spline method is 21. The sampling number is the control point number of the 3rd-order quasi-uniform B-spline, which produces more than 63 parameters in the model. Second, when the Fourier transform model is employed to model the contour of nerve bundles, the optimized order number yields a 4th-order Fourier model, which has 16 parameters. Third, when all contours are modeled by the 4th-order Fourier model, the statistical analysis shows that (1) the pitch parameters a1 and d1 obey the mixed Gaussian distribution; (2) the harmonic parameter b3 obeys the normal distribution; and (3) the pitch parameters b1 and c1 and the remaining harmonic parameters obey the t distribution with position and scale.

Conclusion: This work paves the way for the exploration of the correlation between model parameters and spatial extension.