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•The decisions for matching donor kidneys and recipients affect success of transplantation, but current kidney matching decision procedures do not consider viability loss during preservation.•The objective of this research is to forecast heterogeneous kidney viability, based on historical datasets to support kidney matching decision-making by transferring knowledge from learning the commonality of all kidneys and the heterogeneity of each kidney.•The proposed model provides exactly accurate kidney viability forecasting results compared to the state-of-the-art models including a multitask learning model, a general path model, and a general linear model.•Analytical generalizations gained by modeling have been validated based on needle biopsy data targeting the improvement of tissue extraction accuracy.•The proposed model applied in multiple kidney assessment processes in transplantation can potentially reduce the kidney discard rate by providing effective kidney matching decisions to save more patients.

The simulation and planning system (SPS) requires accurate and real-time feedback regarding the deformation of soft tissues during the needle insertion procedure. Traditional mechanical-based models such as the finite element method (FEM) are widely used to compute the deformations of soft tissue. However, it is difficult for the FEM or other methods to find a balance between an acceptable image fidelity and real-time deformation feedback due to their complex material properties, geometries and interaction mechanisms. In this paper, a Kriging-based method is applied to model the soft tissue deformation to strike a balance between the accuracy and efficiency of deformation feedback. Four combinations of regression and correlation functions are compared regarding their ability to predict the maximum deformations of ten characteristic markers at a fixed insertion depth. The results suggest that a first order regression function with Gaussian correlation functions can best fit the results of the ground truth. The functional response of the Kriging-based method is utilized to model the dynamic deformations of markers at a series of needle insertion depths. The feasibility of the method is verified by investigating the adaptation to step variations. Compared with the ground truth of the finite element (FE) results, the maximum residual is less than 0.92 mm in the Ydirection and 0.31 mm in the Xdirection. The results suggest that the Kriging metamodel provides real-time deformation feedback for a target and an obstacle to a SPS.

Needle–tissue interactive force is vital for developing a virtual simulation and planning system (SPS) and optimizing needle control strategy, wherein the friction coefficient is an important parameter but difficult to pre-determine. The existing studies on friction coefficient estimation were lack of qualitative analysis and did not consider the effect of normal pressure and interactive velocity, which may lead to the inaccuracy of the friction calculation. In this paper, we proposed a novel semiempirical friction coefficient model based on the modified classical elastic friction theory that constructs the relationship between the friction coefficient and parameters such as the normal pressure and velocity. The proposed friction coefficient model is validated by using the computational inverse technique based on coupled finite element material point (CFEMP) contact algorithm. The results show that the friction coefficient between the needle and polyvinyl alcohol (PVA) tissue phantom varies from 0.091 to 0.242 with different normal pressure (7.95–17.80 kPa) and insertion velocity (1–9 mm/s), which agrees well with the experimental data. The results of the paper can help to better understand the intrinsic characteristics of the needle–tissue interactions and optimal needle actuation strategies.

High repeatability of needle insertion experiments is essential to the needle-phantom interaction model validation. However, the influential factors governing the accuracy of the phantom and needle deformations have not been systematically studied. In this paper, the impact of influential factors, including phantom characteristic represented by the ratio of DMSO and thawing time (TT), needle properties represented by needle external diameter (NED) and operating factors such as needle insertion velocity (IV), insertion positions (IP) and repeated insertion times (RITs) are analyzed by orthogonal experiment design. The range calculation shows the most sensitive parameters to phantom deformations are RITs, IV and DMSO while the most sensitive parameters to needle deflection are DMSO, TT and NED. By variance analysis, the significant factors on maximum tissue deformation (MTD) are IV, followed by RITs, DMSO and IP. And NED and TT have nearly no significant impact on MTD. The significant sequence on maximum needle deflection (MND) is as follows: DMSO, TT and NED. Results show that, among all impacting factors, phantom deformation is susceptible to both material properties and operative factors while the needle deflection is more susceptible to material properties of the phantom, which can help researchers in related fields to conduct experiments in a more precise manner and better understand the needle-phantom interaction mechanism.