[Effects on single segmental lumbar stability after graded facetectomy and laminectomy: a finite element study].

OBJECTIVE: To explore the effects on degenerative single segmental lumbar spinal stability after posterior graded laminectomy and facetectomy by a finite element method. METHODS: A finite element model of L3-S1 segments with a single segmental degeneration at L4-5 level was established. Different models of L4-L5 segmental instability after posterior graded laminectomy and facetectomy were established. And interior 1/3, 1/2, 2/3 of laminar and bilateral facet joints were resected. Physical loads were applied to the models and the changes of range of motion (ROM) at L4-5 level in different models were recorded during flexion, extension, lateral bending and rotation. RESULTS: As compared to preoperative model, after resecting 1/3 bilateral facet joints at L4-5 level, L4-5 segmental ROM increased 18% during flexion, 27% during extension and 45% during torsion. And the increased degree of ROM became more obvious along with a greater resection range of bilateral facet joints. No significant difference existed in L4-5 segmental ROM during lateral bending in all models. CONCLUSION: Facet joints play vital roles in lumbar segmental stability. And segmental instability may occur when 1/3 bilateral facet joints are resected.

Approach to feature extraction of ERP / 医疗卫生装备

Feature extraction of event related potential (ERP) plays an important part in both fundamental and clinical researches for cerebral neurophysiology.Based on the prior knowledge of feature distribution of ERP,this paper introduces an approach to feature extraction of ERP from composite EEG,which is combined with wavelet multiresolution analysis (MRA) and radial basis function neural network (RBFNN).The components related to low-frequency response can be extracted from the wavelet decomposition coefficients by RBFNN.Then signal reconstruction is implemented to obtain the feature of ERP.Experimental result demonstrates that the approach is reliable.

Investigation of a Wavelet Transform Based Noise Filtering Approach for Medical Images / 医疗卫生装备

Objective To investigate a wavelet-transform-based approach that reduces the noise of medical images, and to compare the difference of the effects by different wavelet types. Methods A soft threshold approach based on the modification of local coefficient of wavelets was proposed. Firstly, a local modulus extrema distribution of the image, M j,m,n is obtained using wavelet transform. Then the modulus maximum was calculated and a threshold Tm was defined according to the statistical properties of the local modulus extrema distribution. If the extremum of the wavelet transform was greater than or equal to the threshold Tm, the corresponding wavelet coefficient was kept unchanged; while if the extremum of the wavelet transform was less than the threshold Tm, its corresponding wavelet coefficient was calculated using the soft threshold approach. Lastly, an inverse wavelet transform was performed according to the wavelet coefficients of these two parts so that the image could be reconstructed. Results The proposed approach could filter out the noise in medical images effectively, and the effects of noise reduction by different wavelets were different. Conclusion A useful wavelet threshold noise reduction algorithm can be obtained by wavelet multi-dimensional decomposition of image with proper selection of wavelet base function, and comparatively ideal effect of noise reduction can be achieved using this algorithm.