Study on the Low Velocity Stability of a Prostate Seed Implantation Robot’s Rotatory Joint

Modeling and Analysis of Prostate Soft Tissue Puncture Performance using Puncture Needle

When treating prostate cancer, the use of puncture robots is an effective method to perform radioactive seed implantation surgery. However, when the puncture needle enters the lesion, the soft tissue is easily deformed owing to the complex force between the puncture needle and soft tissue, which leads to a puncture deviation between the needle tip and target point. To solve this problem effectively, the prostate soft tissue puncture process is studied based on the analysis of the puncture needle–soft tissue interaction. First, the puncture force is classified into contact, friction, and cutting forces by a quantitative decomposition method, and the corresponding force model is established. Based on the theoretical analysis of the model, it is deduced that these factors can affect the deformation created by puncturing the soft tissue. Subsequently, a puncture platform is built and many biomimetic soft tissue models are established. Multiple puncture experiments on the influencing factors are conducted using the method of controlling a single variable. Using the spatial puncture deviation as the test metrics, the significance of the influencing factors of the puncture deformation is verified. Finally, it can be concluded from the experimental analysis that the main factor that affects the puncture deviation is the puncture speed, whereas the puncture depth has no significant influence. The puncture speed was optimized and verified by experiments, and the results showed that a stable puncture accuracy under different puncture depths can be obtained by selecting an optimized puncture speed (12.6 mm/s). This work provides a design reference to study the positioning accuracy of minimally invasive puncture surgery.

Design of a seed implantation robot with counterbalance and soft tissue stabilization mechanism for prostate cancer brachytherapy

This article focuses on the topic of the structural design of surgical radioactive surgery robot for prostate cancer. To improve the weight-to-payload ratio of surgery robot end-effector, the energy consumption and stability of robot joint drive and reducing the displacement and deformation of needle insertion in soft tissue. This article discusses the new static torque balancing method and multi-needle insertion soft tissue stabilization mechanisms that may be used in previously articulated seed implantation robots. Compared with the existing balancing system schemes, we adopt the idea of mutual conversion of gravitational potential energy and elastic potential energy and establish a static balancing model. With preloaded displacement parameter of the spring α, the variable gravity torque balance of robot arm can be achieved. Torque and equivalent gravity balancing distribution with the spring balance system and the quantitative evaluation experiment were performed, and experiment results provide evidence that these spring balance devices can basically compensate the gravity torque of the robot arm. In addition, we used nonlinear spring–damper model to establish multi-needles insertion soft tissue force model. Then, a variable multi-needle insertion soft tissue stabilization device is designed with six working modes. The innovative design of this device is the use of the first four needles that are introduced simultaneously on either side of the midline. Initially completed displacement simulation of different numbers of needle insertion prostate tissue, experiment results indicate that multi-needle puncture mechanism could reduce prostate displacement in the y- or z-direction. By this method, the prostate may be fixed, thus this mechanism maybe reduces rotation of the prostate and enabling subsequent needles to be inserted accurately.

A Novel Non-Isotonic Statistical Bivariate Regression Method—Application to Stratigraphic Data Modeling and Interpolation

The present paper deals with nonlinear, non-monotonic data regression. This paper introduces an efficient algorithm to perform data transformation from non-monotonic to monotonic to be paired with a statistical bivariate regression method. The proposed algorithm is applied to a number of synthetic and real-world non-monotonic data sets to test its effectiveness. The proposed novel non-isotonic regression algorithm is also applied to a collection of data about strontium isotope stratigraphy and compared to a LOWESS regression tool.