Peripheral nerves are important pathways for receiving afferent sensory impulses and sending out efferent motor instructions, as carried out by sensory nerve fibers and motor nerve fibers. It has remained a great challenge to functionally reconnect nerve internal fiber bundles (or fascicles) in nerve repair. One possible solution may be to establish a 3D nerve fascicle visualization system. This study described the key technology of 3D peripheral nerve fascicle reconstruction. Firstly, fixed nerve segments were embedded with position lines, cryostat-sectioned continuously, stained and imaged histologically. Position line cross-sections were identified using a trained support vector machine method, and the coordinates of their central pixels were obtained. Then, nerve section images were registered using the bilinear method, and edges of fascicles were extracted using an improved gradient vector flow snake method. Subsequently, fascicle types were identified automatically using the multi-directional gradient and second-order gradient method. Finally, a 3D virtual model of internal fascicles was obtained after section images were processed. This technique was successfully applied for 3D reconstruction for the median nerve of the hand-wrist and cubital fossa regions and the gastrocnemius nerve. This nerve internal fascicle 3D reconstruction technology would be helpful for aiding peripheral nerve repair and virtual surgery.

Objective: To conduct mathematical expression of the law of internal fascicular groups of peripheral nerves in spatial extension, so as to reveal the universal law of fascicular groups during the process of spatial extension. Methods: The centroid of each fascicular group shown on each Micro-CT image of the peripheral nerves was extracted, and these centroids were connected to form the centroid space curve of each fascicular group respectively based on preliminary studies. Results: The mean value of relative offset of fascicular groups in space was 3.7 μm while its maximum was: when a distance of 5 μm was extended, the fascicular groups centroid would have an offset of 21.4 μm. The accuracy of fitting of the centroid spatial curve of the fascicular groups using the 4th-order Fourier model could be up to 98%. Each parameter in the model obeyed the t distribution with position/dimension parameters. The dimension of parameters in the 1st-order component was obviously greater than that of the components of the other orders, indicating that the probability density function of harmonic component parameter showed an obvious peak shape. Conclusions: The centroid space curve of the Fascicular groups could express the extension of fascicular groups in space truly and exactly. The extension process of fascicular groups in space could be expressed accurately by the 4th-order Fourier model. The reason for using the 4th-order model was that a better balance could be obtained in model complexity and accuracy of fitting.

With the wide utilization of wireless sensor networks(WSN), higher reliability and stability are being pursued gradually. In most cases, the communication capability for sensor network is influenced by the complex environmental conditions, the open characteristics of channels, the energy limitations of nodes, as well as network protocol design issues, ultimately leading to the high possibility of network failure. As a result, a timely and accurate fault diagnosis is of much significance for a network to ensure the stable operation and execution efficiency. This article firstly demonstrates the diagnostic process on the following three aspects, including the collection for network fault information, fault detection, and diagnosis process. In addition, the features of commonly used technologies are also analyzed and compared in order to identify their application scope respectively. Finally, this paper makes the summary for the possible development trends and future research directions of fault diagnosis.

The objective is to explore the appropriate method to establish the mathematical model of fascicular groups' contours from micro-CT images of peripheral nerves during the nonsplitting/merging phase. The original contours of fascicular groups from the micro-CT image were described as the discrete pixel points. All discrete pixel points of shapes were extracted into a data set through image processing. The data set was modeled by Bezier, B-spline method, respectively, in which each discrete point was used as a control point for modeling. In the Bezier method, the contour of a nerve bundle needs more than two different Bezier curves to model, making the junction points between two models discontinuous. The contour model described by B-spline is very close to the original contour of nerve bundles when all discrete points are used as the control points. The models described by B-spline have different terms and parameters, making it difficult to calculate in the following research. When the third-order quasi-uniform B-spline method is employed, all nerve bundles models have the same number of terms. The modeling error of third-order quasi-uniform B-spline is less than 3% when the Dice coefficient is more than 95%, and the appropriate number of sampling times is 21. The modeling accuracy is improved with increased sampling times when it is less than 21. However, the modeling accuracy remains stable while the number of sampling times is more than 21. The third-order quasi-uniform B-spline is more efficient in modeling nerve bundles' contour, which is more accurate and straightforward.

No abstract