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Background and objectiveOptical coherence tomography (OCT) has been used in limited settings to study peripheral nerve injury. The purpose of the study is to determine whether high-resolution OCT can be used to monitor nerve injury and regeneration in the rat sciatic nerve following crush injury, ligation, and transection with microsurgical repair.Study design/materials and methodsForty-five rats were segregated into three groups. The right sciatic nerve was suture ligated (n = 15), cut then microsurgically repaired (n = 15), or crushed (n = 15). The left sciatic nerve served as the control; only surgical exposure and skin closure were performed. Each group was further divided into three subgroups where they were assigned survival durations of 4, 15, or 24 weeks. Following euthanasia, nerves were harvested, fixed in formalin, and imaged at the injury site, as well as proximal and distal ends. The OCT system resolution was approximately 7 microm in tissue with a 1,060 nm central wavelength.ResultsControl (uninjured) nerve tissue showed homogenous signal distribution to a relatively uniform depth; in contrast, damaged nerves showed irregular signal distribution and intensity. Changes in signal distribution were most significant at the injury site and distal regions. Increases in signal irregularity were evident during longer recovery times. Histological analysis determined that OCT imaging was limited to the surrounding perineurium and scar tissue.ConclusionOCT has the potential to be a valuable tool for monitoring nerve injury and repair, and the changes that accompany wound healing, providing clinicians with a non-invasive tool to treat nerve injuries.

Ground-penetrating radar (GPR) is a nondestructive testing technology that is widely applied in infrastructure maintenance, archaeological research, military operations, and other geological studies. A crucial step in GPR data processing is the detection and classification of underground structures and buried objects, including reinforcement bars, landmines, pipelines, bedrock, and underground cavities. With the development of machine learning algorithms, traditional methods such as SVM, K-NN, ANN, and HMM, as well as deep learning algorithms, have gradually been incorporated into A-scan, B-scan, and C-scan GPR image processing. This paper provides a summary of the typical machine learning and deep learning algorithms employed in the field of GPR and categorizes them based on the feature extraction method or classifier used. Additionally, this work discusses the sources and forms of data utilized in these studies. Finally, potential future development directions are presented. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR), as a non-destructive and rapid detection instrument, has been widely used for underground pipeline detection. However, as the interpretation of 3-dimensional GPR images is still manually performed, the process is inefficient. Aiming at solving the challenges of automatic recognition for underground pipelines, we propose a recognition method based on a deep learning algorithm, which uses 3-dimensional GPR images and the improved 3D depth-wise separable convolution block. In order to expand the number of samples in the dataset, we propose a data augmentation method based on three-dimensional matrix rotation and use a wavelet-based denoising method to filter out the direct wave interference. To prove the effectiveness and efficiency of our method, we compared the classification performance of the improved 3D depth-wise separable convolutional block with the traditional 3D convolutional block and the ordinary 3D depth-wise separable convolutional block under the same conditions. According to the experiment's results, the number of parameters of the method we proposed is 66.9% less than that of the traditional 3D convolution method, while the classification performance is similar. Furthermore, compared with ordinary 3D depth-wise separable convolution, our method can significantly improve the classification and recognition ability of the neural network, while the number of calculations and the number of parameters remain almost the same. This study demonstrates the effectiveness of 3D-CNN in the field of GPR image interpretation. An improved 3D depth-wise separable convolutional block is also proposed. It greatly reduces the amount of calculation and parameters while ensuring classification performance. It is better than the existing algorithms in performance. At the same time, to obtain the position and direction of the pipeline, in this study, a conic fitting method using the Canny operator is proposed to locate the vertices of B-Scan images and record their horizontal and vertical coordinates. This method can estimate the direction of the pipeline and it lays the foundation for future work such as measuring the pipeline depth. [ABSTRACT FROM AUTHOR]