Subject-specific non-linear biomechanical model of needle insertion into brain

Experimental Study of the Implantation Process for Array Electrodes into Highly Transparent Agarose Gel

Brain-computer interface (BCI) technology is currently a cutting-edge exploratory problem in the field of human-computer interaction. However, in experiments involving the implantation of electrodes into brain tissue, particularly high-speed or array implants, existing technologies find it challenging to observe the damage in real time. Considering the difficulties in obtaining biological brain tissue and the challenges associated with real-time observation of damage during the implantation process, we have prepared a transparent agarose gel that closely mimics the mechanical properties of biological brain tissue for use in electrode implantation experiments. Subsequently, we developed an experimental setup for synchronized observation of the electrode implantation process, utilizing the Digital Gradient Sensing (DGS) method. In the single electrode implantation experiments, with the increase in implantation speed, the implantation load increases progressively, and the tissue damage region around the electrode tip gradually diminishes. In the array electrode implantation experiments, compared to a single electrode, the degree of tissue indentation is more severe due to the coupling effect between adjacent electrodes. As the array spacing increases, the coupling effect gradually diminishes. The experimental results indicate that appropriately increasing the velocity and array spacing of the electrodes can enhance the likelihood of successful implantation. The research findings of this article provide valuable guidance for the damage assessment and selection of implantation parameters during the process of electrode implantation into real brain tissue.

Design of composite puncture blood collection system and research on puncture force

Venous blood collection testing is one of the most commonly used medical diagnostic methods. Compared with conventional venous blood collection, robotic collection can reduce needle-stick injuries, medical staff workload, and infection risk; allow doctor-patient isolation; and improve collection reliability. Existing venous blood collection robots use rigid puncture needles, which can easily puncture the lower wall of blood vessels, causing vessel damage and collection failure. This paper proposes a bionic blood collection strategy based on a composite puncture needle that mimics the structure and function of mosquito mouthparts. A bionic composite puncture needle insertion system with puncture-force sensing was designed, and venipuncture forces were simulated and mathematically modelled. A prototype insertion system was built and used in an experiment, which demonstrated effective composite puncture blood collection and explored the factors influencing puncture force. Puncture force decreases with increased puncture speed and angle and with a decreased needle diameter. This provides a basis for optimising the parameters of blood collection robots.

Mathematical modeling and computer simulation of needle insertion into soft tissue

In this study we present a kinematic approach for modeling needle insertion into soft tissues. The kinematic approach allows the presentation of the problem as Dirichlet-type (i.e. driven by enforced motion of boundaries) and therefore weakly sensitive to unknown properties of the tissues and needle-tissue interaction. The parameters used in the kinematic approach are straightforward to determine from images. Our method uses Meshless Total Lagrangian Explicit Dynamics (MTLED) method to compute soft tissue deformations. The proposed scheme was validated against experiments of needle insertion into silicone gel samples. We also present a simulation of needle insertion into the brain demonstrating the method's insensitivity to assumed mechanical properties of tissue.

Suite of meshless algorithms for accurate computation of soft tissue deformation for surgical simulation

The ability to predict patient-specific soft tissue deformations is key for computer-integrated surgery systems and the core enabling technology for a new era of personalized medicine. Element-Free Galerkin (EFG) methods are better suited for solving soft tissue deformation problems than the finite element method (FEM) due to their capability of handling large deformation while also eliminating the necessity of creating a complex predefined mesh. Nevertheless, meshless methods based on EFG formulation, exhibit three major limitations: (i) meshless shape functions using higher order basis cannot always be computed for arbitrarily distributed nodes (irregular node placement is crucial for facilitating automated discretization of complex geometries); (ii) imposition of the Essential Boundary Conditions (EBC) is not straightforward; and, (iii) numerical (Gauss) integration in space is not exact as meshless shape functions are not polynomial. This paper presents a suite of Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithms incorporating a Modified Moving Least Squares (MMLS) method for interpolating scattered data both for visualization and for numerical computations of soft tissue deformation, a novel way of imposing EBC for explicit time integration, and an adaptive numerical integration procedure within the Meshless Total Lagrangian Explicit Dynamics algorithm. The appropriateness and effectiveness of the proposed methods is demonstrated using comparisons with the established non-linear procedures from commercial finite element software ABAQUS and experiments with very large deformations. To demonstrate the translational benefits of MTLED we also present a realistic brain-shift computation.

Prediction of brain deformations and risk of traumatic brain injury due to closed-head impact: quantitative analysis of the effects of boundary conditions and brain tissue constitutive model

In this study, we investigate the effects of modelling choices for the brain-skull interface (layers of tissues between the brain and skull that determine boundary conditions for the brain) and the constitutive model of brain parenchyma on the brain responses under violent impact as predicted using computational biomechanics model. We used the head/brain model from Total HUman Model for Safety (THUMS)-extensively validated finite element model of the human body that has been applied in numerous injury biomechanics studies. The computations were conducted using a well-established nonlinear explicit dynamics finite element code LS-DYNA. We employed four approaches for modelling the brain-skull interface and four constitutive models for the brain tissue in the numerical simulations of the experiments on post-mortem human subjects exposed to violent impacts reported in the literature. The brain-skull interface models included direct representation of the brain meninges and cerebrospinal fluid, outer brain surface rigidly attached to the skull, frictionless sliding contact between the brain and skull, and a layer of spring-type cohesive elements between the brain and skull. We considered Ogden hyperviscoelastic, Mooney-Rivlin hyperviscoelastic, neo-Hookean hyperviscoelastic and linear viscoelastic constitutive models of the brain tissue. Our study indicates that the predicted deformations within the brain and related brain injury criteria are strongly affected by both the approach of modelling the brain-skull interface and the constitutive model of the brain parenchyma tissues. The results suggest that accurate prediction of deformations within the brain and risk of brain injury due to violent impact using computational biomechanics models may require representation of the meninges and subarachnoidal space with cerebrospinal fluid in the model and application of hyperviscoelastic (preferably Ogden-type) constitutive model for the brain tissue.

A new method for piercing the tentorium cerebelli for implanting fragile electrodes into the brain stem in the rhesus monkey (Macaca mulatta)

Recent developments in neuron recording techniques include the invention of some fragile electrodes. The fragility of these electrodes impedes their successful use in deep brain recordings because it is difficult to penetrate the electrodes through the dura mater, especially the tentorium cerebelli (TC) enclosing the cerebellum and brain stem. This paper reports a new method to pierce the TC for inserting fragile electrodes into the inferior colliculus of rhesus monkeys. Briefly, a unique tool kit, consisting of needles with sharp tips, a guide tube and an “impactor,” was used in a multistep protocol to pierce the TC. The impactor provided a brief force that quickly thrusts the needles through the meninges without causing significant damage to the brain tissue under the TC. Using this novel approach, tetrodes were successfully implanted into the inferior colliculus of a rhesus monkey and neuronal discharge signals were recorded. This method, which is simple, convenient and economical, allows neurophysiologists to study the electrophysiological characteristics of deep brain structures under the TC with advanced, albeit fragile, electrodes.

Long-term changes in the material properties of brain tissue at the implant-tissue interface

OBJECTIVE

Brain tissue undergoes dramatic molecular and cellular remodeling at the implant-tissue interface that evolves over a period of weeks after implantation. The biomechanical impact of such remodeling on the interface remains unknown. In this study, we aim to assess the changes in the mechanical properties of the brain-electrode interface after chronic implantation of a microelectrode.

APPROACH

Microelectrodes were implanted in the rodent cortex at a depth of 1 mm for different durations-1 day (n = 4), 10-14 days (n = 4), 4 weeks (n = 4) and 6-8 weeks (n = 7). After the initial duration of implantation, the microelectrodes were moved an additional 1 mm downward at a constant speed of 10 µm s(-1). Forces experienced by the microelectrode were measured during movement and after termination of movement. The biomechanical properties of the interfacial brain tissue were assessed from measured force-displacement curves using two separate models-a two-parameter Mooney-Rivlin hyperelastic model and a viscoelastic model with a second-order Prony series.

MAIN RESULTS

Estimated shear moduli using a second-order viscoelastic model increased from 0.5-2.6 kPa (day 1 of implantation) to 25.7-59.3 kPa (after 4 weeks of implantation) and subsequently decreased to 0.8-7.9 kPa after 6-8 weeks of implantation in 6 of the 7 animals. The estimated elastic modulus increased from 4.1-7.8 kPa on the day of implantation to 24-44.9 kPa after 4 weeks. The elastic modulus was estimated to be 6.8-33.3 kPa in 6 of the 7 animals after 6-8 weeks of implantation. The above estimates suggest that the brain tissue surrounding the microelectrode evolves from a stiff matrix with maximal shear and elastic modulus after 4 weeks of implantation into a composite of two different layers with different mechanical properties-a stiff compact inner layer surrounded by softer brain tissue that is biomechanically similar to brain tissue-during the first week of implantation. Tissue micromotion-induced stresses on the microelectrode constituted 12-55% of the steady-state stresses on the microelectrode on the day of implantation (n = 4), 2-21% of the steady-state stresses after 4 weeks of implantation (n = 4), and 4-10% of the steady-state stresses after 6-8 weeks of implantation (n = 7).

SIGNIFICANCE

Understanding biomechanical behavior at the brain-microelectrode interface is necessary for the long-term success of implantable neuroprosthetics and microelectrode arrays. Such quantitative physical characterization of the dynamic changes in the electrode-tissue interface will (a) drive the design and development of more mechanically optimal, chronic brain implants, and (b) lead to new insights into key cellular and molecular events such as neuronal adhesion, migration and function in the immediate vicinity of the brain implant.

Coaxial needle insertion assistant with enhanced force feedback

Many medical procedures involving needle insertion into soft tissues, such as anesthesia, biopsy, brachytherapy, and placement of electrodes, are performed without image guidance. In such procedures, haptic detection of changing tissue properties at different depths during needle insertion is important for needle localization and detection of subsurface structures. However, changes in tissue mechanical properties deep inside the tissue are difficult for human operators to sense, because the relatively large friction force between the needle shaft and the surrounding tissue masks the smaller tip forces. A novel robotic coaxial needle insertion assistant, which enhances operator force perception, is presented. This one-degree-of-freedom cable-driven robot provides to the operator a scaled version of the force applied by the needle tip to the tissue, using a novel design and sensors that separate the needle tip force from the shaft friction force. The ability of human operators to use the robot to detect membranes embedded in artificial soft tissue was tested under the conditions of 1) tip force and shaft force feedback, and 2) tip force only feedback. The ratio of successful to unsuccessful membrane detections was significantly higher (up to 50%) when only the needle tip force was provided to the user.

Meshless algorithm for soft tissue cutting in surgical simulation

Computation of soft tissue mechanical responses for surgery simulation and image-guided surgery has been dominated by the finite element (FE) method that utilises a mesh of interconnected elements as a computational grid. Shortcomings of such mesh-based discretisation in modelling of surgical cutting include high computational cost and the need for re-meshing in the vicinity of cutting-induced discontinuity. The meshless total Lagrangian adaptive dynamic relaxation (MTLADR) algorithm we present here does not exhibit such shortcomings, as it relies on spatial discretisation in a form of a cloud of nodes. The cutting-induced discontinuity is modelled solely through changes in nodal domains of influence, which is done through efficient implementation of the visibility criterion using the level set method. Accuracy of our MTLADR algorithm with visibility criterion is confirmed against the established nonlinear solution procedures available in the commercial FE code Abaqus.

Needle-tissue interaction forces--a survey of experimental data

The development of needles, needle-insertion simulators, and needle-wielding robots for use in a clinical environment depends on a thorough understanding of the mechanics of needle-tissue interaction. It stands to reason that the forces arising from this interaction are influenced by numerous factors, such as needle type, insertion speed, and tissue characteristics. However, exactly how these factors influence the force is not clear. For this reason, the influence of various factors on needle insertion-force was investigated by searching literature for experimental data. This resulted in a comprehensive overview of experimental insertion-force data available in the literature, grouped by factor for quick reference. In total, 99 papers presenting such force data were found, with typical peak forces in the order of 1-10N. The data suggest, for example, that higher velocity tends to decrease puncture force and increase friction. Furthermore, increased needle diameter was found to increase peak forces, and conical needles were found to create higher peak forces than beveled needles. However, many questions remain open for investigation, especially those concerning the influence of tissue characteristics.

Force feedback in a piezoelectric linear actuator for neurosurgery

BACKGROUND

Force feedback in robotic minimally invasive surgery allows the human operator to manipulate tissues as if his/her hands were in contact with the patient organs. A force sensor mounted on the probe raises problems with sterilization of the overall surgical tool. Also, the use of off-axis gauges introduces a moment that increases the friction force on the bearing, which can easily mask off the signal, given the small force to be measured.

METHODS

This work aims at designing and testing two methods for estimating the resistance to the advancement (force) experienced by a standard probe for brain biopsies within a brain-like material. The further goal is to provide a neurosurgeon using a master-slave tele-operated driver with direct feedback on the tissue mechanical characteristics. Two possible sensing methods, in-axis strain gauge force sensor and position-position error (control-based method), were implemented and tested, both aimed at device miniaturization. The analysis carried out was aimed at fulfilment of the psychophysics requirements for force detection and delay tolerance, also taking into account safety, which is directly related to the last two issues. Controller parameters definition is addressed and consideration is given to development of the device with integration of a haptic interface.

RESULTS

Results show better performance of the control-based method (RMSE < 0.1 N), which is also best for reliability, sterilizability, and material dimensions for the application addressed.

CONCLUSIONS

The control-based method developed for force estimation is compatible with the neurosurgical application and is also capable of measuring tissue resistance without any additional sensors. Force feedback in minimally invasive surgery allows the human operator to manipulate tissues as if his/her hands were in contact with the patient organs.

Development of in vivo constitutive models for liver: application to surgical simulation

Advancements in real-time surgical simulation techniques have provided the ability to utilize more complex nonlinear constitutive models for biological tissues which result in increased haptic and graphic accuracy. When developing such a model, verification is necessary to determine the accuracy of the force response as well as the magnitude of tissue deformation for tool-tissue interactions. In this study, we present an experimental device which provides the ability to obtain force-displacement information as well as surface deformation of porcine liver for in vivo probing tasks. In addition, the system is capable of accurately determining the geometry of the liver specimen. These combined attributes provide the context required to simulate the experiment with accurate boundary conditions, whereby the only variable in the analysis is the material properties of the liver specimen. During the simulation, effects of settling due to gravity have been taken into account by a technique which incorporates the proper internal stress conditions in the model without altering the geometry. Initially, an Ogden model developed from ex vivo tension and compression experimentation is run through the simulation to determine the efficacy of utilizing an ex vivo model for simulation of in vivo probing tasks on porcine liver. Subsequently, a method for improving upon the ex vivo model was developed using different hyperelastic models such that increased accuracy could be achieved for the force characteristics compared to the displacement characteristics, since changes in the force variation would be more perceptible to a user in the simulation environment, while maintaining a high correlation with the surface displacement data. Furthermore, this study also presents the probing simulation which includes the capsule surrounding the liver.

Modeling, Simulation, and Optimal Initiation Planning For Needle Insertion Into the Liver

Needle insertion simulation and planning systems (SPSs) will play an important role in diminishing inappropriate insertions into soft tissues and resultant complications. Difficulties in SPS development are due in large part to the computational requirements of the extensive calculations in finite element (FE) models of tissue. For clinical feasibility, the computational speed of SPSs must be improved. At the same time, a realistic model of tissue properties that reflects large and velocity-dependent deformations must be employed. The purpose of this study is to address the aforementioned difficulties by presenting a cost-effective SPS platform for needle insertions into the liver. The study was constrained to planar (2D) cases, but can be extended to 3D insertions. To accommodate large and velocity-dependent deformations, a hyperviscoelastic model was devised to produce an FE model of liver tissue. Material constants were identified by a genetic algorithm applied to the experimental results of unconfined compressions of bovine liver. The approach for SPS involves B-spline interpolations of sample data generated from the FE model of liver. Two interpolation-based models are introduced to approximate puncture times and to approximate the coordinates of FE model nodes interacting with the needle tip as a function of the needle initiation pose; the latter was also a function of postpuncture time. A real-time simulation framework is provided, and its computational benefit is highlighted by comparing its performance with the FE method. A planning algorithm for optimal needle initiation was designed, and its effectiveness was evaluated by analyzing its accuracy in reaching a random set of targets at different resolutions of sampled data using the FE model. The proposed simulation framework can easily surpass haptic rates (>500 Hz), even with a high pose resolution level (∼30). The computational time required to update the coordinates of the node at the needle tip in the provided example was reduced from 177 s to 0.8069 ms. The planning accuracy was acceptable even with moderate resolution levels: root-mean-square and maximum errors were 1 mm and 1.2 mm, respectively, for a pose and PPT resolution levels of 17 and 20, respectively. The proposed interpolation-based models significantly improve the computational speed of needle insertion simulation and planning, based on the discretized (FE) model of the liver and can be utilized to establish a cost-effective planning platform. This modeling approach can also be extended for use in other surgical simulations.

Current progress in patient-specific modeling

We present a survey of recent advancements in the emerging field of patient-specific modeling (PSM). Researchers in this field are currently simulating a wide variety of tissue and organ dynamics to address challenges in various clinical domains. The majority of this research employs three-dimensional, image-based modeling techniques. Recent PSM publications mostly represent feasibility or preliminary validation studies on modeling technologies, and these systems will require further clinical validation and usability testing before they can become a standard of care. We anticipate that with further testing and research, PSM-derived technologies will eventually become valuable, versatile clinical tools.