Research on the Contour Modeling Method of Peripheral Nerve Internal Fascicular Groups During the Non-Splitting/Merging Phase and Distribution Rules of Model Parameters - Zhong, Yingchun; Tian, Zhihao; Luo, Peng; Sun, Siyu; Zhu, Shuang (Frontiers in Cellular Neuroscience, 2022)
Objectives: To investigate benchmark data for docking the same functional nerve bundles based on the mathematic contour model of peripheral nerve internal fascicular groups. Material and methods: First, the discrete points of the original contours of nerve bundles were extracted into a data set through the image process. Second, two indicators were employed to evaluate the modeling precision. Third, the data set was modeled by the 3-order quasi-uniform B-spline method. Fourth, the data set was modeled by the method from the way of Fourier Transform. Fifth, all contours were modeled by the 4-order Fourier method. Then the histogram of each parameter from the Fourier model was calculated. Furthermore, the probability density function of each parameter was fitted. Results: First, the optimized sampling number of the 3-order quasi-uniform B-spline method is 21. The sampling number is the control point number of the 3-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 of the Fourier model is 4-order, which has 16 parameters. Third, when all contours are modeled by the 4-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; (3)the pitch parameters b1, c1, and the rest harmonic parameters obey the t Distribution with position and scale. Conclusions: This work paves the way for the exploration of the correlation between model parameters and spatial extension.