Polyvinyl alcohol (PVA) hydrogel was made for simulating human's soft tissue in our experiment. The image acquisition device is composed of an optical platform, a camera and its bracket and a light source. In order to study the law of soft tissue deformation under flexible needle insertion, markers were embedded into the soft tissue and their displacements were recorded. Based on the analysis of displacements of markers in X direction and Y direction, back propagation (BP) neural network was employed to model the displacement of Y direction for the markers. Compared to the experimental data, fitting degree of the neural network model was above 95%, the maximum relative error for valid data was limited to 30%, and the maximum absolute error was 0.8 mm. The BP neural network model was beneficial for predicting soft tissue deformation quantitatively. The results showed that the model could effectively improve the accuracy of flexible needle insertion into soft tissue.
Citation： GAODedong, ZHAOGuangwei, WANGShan, ZHUTong. Study on Prediction Model of Soft Tissue Deformation during Needle Insertion. Journal of Biomedical Engineering, 2016, 33(3): 442-447. doi: 10.7507/1001-5515.20160075 Copy