Feasibility of extracting tissue material properties via cohesive elements: a finite element approach to probe insertion procedures in non-invasive spine surgeries

System identification and simulation of soft tissue force feedback in a spine surgical simulator

Surgical simulators are being introduced as training modalities for surgeons. This paper aims to evaluate dynamic models used to convey force feedback from puncturing the soft tissue during a spine surgical simulation. The force feedback of the tissue is treated as a dynamic system. This is done by performing classical system identification across a bandwidth of frequencies on a tissue analogue and fitting that behaviour to dynamic viscoelastic models. The models that are tested are an inverted linear model, the Maxwell model, the Kelvin-Boltzmann (KB) model, and a higher-order blackbox (HO) model. Several error metrics such as percent variance accounted for (%VAF) are determined to measure solution accuracy. The force feedback models are programmed into a surgical simulator and tested with study participants who rated them based on how well the identified models match the behaviour of the rubber tissue analogue. The highest %VAF is 82.64% when the tissue is modelled as the HO model. Statistically significant differences (p < 0.05) are found between all model ratings from participants except between the HO model and the KB model. However, the HO model has the highest percentage (37.8%) of participants who rank its performance as the closest to the tissue analogue compared to the other force feedback models. The more accurately the dynamic behaviour resembles the tissue analogue, the higher the model was rated by study participants. This study highlights the importance of utilizing dynamic signals to generate dynamic models of soft tissue for spine surgical simulators.

Optimizing autoinjector devices using physics-based simulations and Gaussian processes

Autoinjectors are becoming a primary drug delivery option to the subcutaneous space. These devices need to work robustly and autonomously to maximize drug bio-availability. However, current designs ignore the coupling between autoinjector dynamics and tissue biomechanics. Here we present a Bayesian framework for optimization of autoinjector devices that can account for the coupled autoinjector-tissue biomechanics and uncertainty in tissue mechanical behavior. The framework relies on replacing the high fidelity model of tissue insertion with a Gaussian process (GP). The GP model is accurate yet computationally affordable, enabling a thorough sensitivity analysis that identified tissue properties, which are not part of the autoinjector design space, as important variables for the injection process. Higher fracture toughness decreases the crack depth, while tissue shear modulus has the opposite effect. The sensitivity analysis also shows that drug viscosity and spring force, which are part of the design space, affect the location and timing of drug delivery. Low viscosity could lead to premature delivery, but can be prevented with smaller spring forces, while higher viscosity could prevent premature delivery while demanding larger spring forces and increasing the time of injection. Increasing the spring force guarantees penetration to the desired depth, but it can result in undesirably high accelerations. The Bayesian optimization framework tackles the challenge of designing devices with performance metrics coupled to uncertain tissue properties. This work is important for the design of other medical devices for which optimization in the presence of material behavior uncertainty is needed.

Development and evaluation of a numerical spine model comprising intra-abdominal pressure for use in assessing physiological changes on abdominal compliance and spinal stability

BACKGROUND

Abdominal compliance is the "measure of ease of abdominal expansion" and determines whether a patient can withstand high intra-abdominal pressures. Thus, high compliance indicates that the abdomen can expand relatively freely, while low compliance restricts abdominal expansion. The global objective of the present work is to evaluate the effect of physiological changes on abdominal compliance using a comprehensive spine finite element model inclusive of intra-abdominal pressure.

METHODS

The effect of changing Young's modulus, abdominal wall thickness, and abdominal radii on abdominal compliance were evaluated. Intra-abdominal pressure and thoracolumbar fascia forces were also evaluated to assess abdominal physiological changes effects on overall static spinal stability.

FINDINGS

Results showed that as wall thickness increased, compliance decreased. Similar findings were made with an increase in abdominal radius and Young's modulus. Furthermore, the active reduction in compliance, caused by increased elasticity and abdominal radius, resulted in an increase in spinal supportive forces originating from the thoracolumbar fascia and intra-abdominal pressurization, along with an increase in spine displacement from its original stable position. There was no clear stability trend for the case of changing abdominal wall thickness as fluctuations were present.

INTERPRETATION

Investigated mechanics and data trends suggested that dangerously low compliance levels might result from poor abdominal elasticity and thickening fat layers. This led to a direct discussion and recommendations for obesity conditions and laparoscopy applications. Lastly, static spinal stability showed to improve through increasing active abdominal compliance by means of actively engaging abdominal pressure, hence augmenting abdominal active elasticity.

Formulation and exploration of novel, intramuscular pressure based, muscle activation strategies in a spine model

Optimization models are often devised to assess spinal stability via estimating individual muscle forces. However, neglecting muscles' fluidic behavior remains an approximation due to the role of muscle pressure in force transmission. The purpose of this study was to leverage a validated Finite Element (FE) model of the spine, inclusive of Intra-Muscular Pressure (IMP), to explore muscle activation strategies towards maintaining equilibrium spinal stability. Three conventional strategies governing minimizing muscle effort, minimizing IVD compressive forces, and maintaining stability at all costs were first investigated to explore model's validity. Thereafter, two novel IMP-based strategies were devised and explored, specifically minimizing and maximizing IMP. The model was previously shown valid in light of in vivo and in silico observations with an average discrepancy of 6%. This being the case, the conventional strategies dictated efficacy in muscular activations whilst maintaining an equilibrium stable position, as quantified in the present paper, with a difference of 9.8% from documented data. In addition, the explored novel IMP-based strategies suggested the presence of a threshold individual muscles IMP, approximately 272 mmHg for the longissimus muscle for example, beyond which muscles potentially start to share radial loads with surrounding tissues, whilst limiting the contraction of the underlying muscles. In conclusion, this study theoretically supports the possibility of activation strategies based on muscular pressure, which the developed, verified, and validated FE spine model was leveraged to investigate. The explored novel IMP-based strategies may have significance in informing clinical applications such as motion analysis and functional electrical stimulation of muscles.