The Design and Construction of a Bulge Testing Device Platform for Human Skin Tissue Applications

Limited standard mechanical testing practises and stress-strain data are available for anisotropic human skin tissue in biaxial loading configurations to suitably represent skin in vivo. Inconsistencies in mechanical and physical properties in the literature due to numerous physiological factors have restricted development of biaxial testing equipment in laboratories to ad hoc research solutions having limited modifiability and parametric control. This project aims to develop a biaxial tensile testing device and testing platform which can be used in a research laboratory setting to provide a springboard to expediate mechanical skin tissue testing. The device can be easily reconfigured to accommodate a range of bulge pressures, while being driven via a 10bar compressed air supply. Based on simplified modelling of skin as an elastomer, mechanical and pneumatic resistivecapacitive pressure vessel models are developed. These are used respectively to initially specify a modifiable piston-cylinder bulge testing apparatus, and to design a customisable discrete proportional-integral closed-loop feedback pressurisation rate control system and software control environment. Pressure-time histories were successfully collected and stored on a dedicated computer for silicone sheet samples of 50mm diameter, as a surrogate for skin, that were tested using the platform to maximum pressures of about 200 kPa, at rates set between 2 20 kPa/s. The efficacy of the rate control system was affected by resolution of discrete pressurisation components that were used. The described platform is currently suitable for controlled and measured bulge pressurisation of elastomers. It is recommended to extend facility of the current platform by integrating 3D imaging and measurement technologies, to evaluate deformation of bulged anisotropic skin tissue and map inhomogeneous stress-strain fields for complex tensile stress-strain evaluations

Robotically Steered Needles: A Survey of Neurosurgical Applications and Technical Innovations

This paper surveys both the clinical applications and main technical innovations related to steered needles, with an emphasis on neurosurgery. Technical innovations generally center on curvilinear robots that can adopt a complex path that circumvents critical structures and eloquent brain tissue. These advances include several needle-steering approaches, which consist of tip-based, lengthwise, base motion-driven, and tissue-centered steering strategies. This paper also describes foundational mathematical models for steering, where potential fields, nonholonomic bicycle-like models, spring models, and stochastic approaches are cited. In addition, practical path planning systems are also addressed, where we cite uncertainty modeling in path planning, intraoperative soft tissue shift estimation through imaging scans acquired during the procedure, and simulation-based prediction. Neurosurgical scenarios tend to emphasize straight needles so far, and span deep-brain stimulation (DBS), stereoelectroencephalography (SEEG), intracerebral drug delivery (IDD), stereotactic brain biopsy (SBB), stereotactic needle aspiration for hematoma, cysts and abscesses, and brachytherapy as well as thermal ablation of brain tumors and seizure-generating regions. We emphasize therapeutic considerations and complications that have been documented in conjunction with these applications

Sensorisation of a novel biologically inspired flexible needle

Percutaneous interventions are commonly performed during minimally invasive brain surgery, where a straight rigid instrument is inserted through a small incision to access a deep lesion in the brain. Puncturing a vessel during this procedure can be a life-threatening complication. Embedding a forward-looking sensor in a rigid needle has been proposed to tackle this problem; however, using a rigid needle, the procedure needs to be interrupted if a vessel is detected. Steerable needle technology could be used to avoid obstacles, such as blood vessels, due to its ability to follow curvilinear paths, but research to date was lacking in this respect. This thesis aims to investigate the deployment of forward-looking sensors for vessel detection in a steerable needle. The needle itself is based on a bioinspired programmable bevel-tip needle (PBN), a multi-segment design featuring four hollow working channels. In this thesis, laser Doppler flowmetry (LDF) is initially characterised to ensure that the sensor fulfils the minimum requirements for it to be used in conjunction with the needle. Subsequently, vessel reconstruction algorithms are proposed. To determine the axial and off-axis position of the vessel with respect to the probe, successive measurements of the LDF sensor are used. Ideally, full knowledge of the vessel orientation is required to execute an avoidance strategy. Using two LDF probes and a novel signal processing method described in this thesis, the predicted possible vessel orientations can be reduced to four, a setup which is explored here to demonstrate viable obstacle detection with only partial sensor information. Relative measurements from four LDF sensors are also explored to classify possible vessel orientations in full and without ambiguity, but under the assumption that the vessel is perpendicular to the needle insertion axis. Experimental results on a synthetic grey matter phantom are presented, which confirm these findings. To release the perpendicularity assumption, the thesis concludes with the description of a machine learning technique based on a Long Short-term memory network, which enables a vessel's spatial position, cross-sectional diameter and full pose to be predicted with sub-millimetre accuracy. Simulated and in-vitro examinations of vessel detection with this approach are used to demonstrate effective predictive ability. Collectively, these results demonstrate that the proposed steerable needle sensorisation is viable and could lead to improved safety during robotic assisted needle steering interventions.Open Acces

Characterisation and State Estimation of Magnetic Soft Continuum Robots

Minimally invasive surgery has become more popular as it leads to less bleeding, scarring, pain, and shorter recovery time. However, this has come with counter-intuitive devices and steep surgeon learning curves. Magnetically actuated Soft Continuum Robots (SCR) have the potential to replace these devices, providing high dexterity together with the ability to conform to complex environments and safe human interactions without the cognitive burden for the clinician. Despite considerable progress in the past decade in their development, several challenges still plague SCR hindering their full realisation. This thesis aims at improving magnetically actuated SCR by addressing some of these challenges, such as material characterisation and modelling, and sensing feedback and localisation. Material characterisation for SCR is essential for understanding their behaviour and designing effective modelling and simulation strategies. In this work, the material properties of commonly employed materials in magnetically actuated SCR, such as elastic modulus, hyper-elastic model parameters, and magnetic moment were determined. Additionally, the effect these parameters have on modelling and simulating these devices was investigated. Due to the nature of magnetic actuation, localisation is of utmost importance to ensure accurate control and delivery of functionality. As such, two localisation strategies for magnetically actuated SCR were developed, one capable of estimating the full 6 degrees of freedom (DOFs) pose without any prior pose information, and another capable of accurately tracking the full 6-DOFs in real-time with positional errors lower than 4~mm. These will contribute to the development of autonomous navigation and closed-loop control of magnetically actuated SCR

MODELLING AND IN VIVO MONITORING OF THE TIME DEPENDENT MECHANICAL PROPERTIES OF TISSUE ENGINEERING SCAFFOLDS

When organs and tissue fail either due to pre-existing disease progression or by accidental damage, current state of the art treatment involves the replacement of the damaged or diseased tissue with new donor derived organs/tissue. The limitations of these current approaches include a limited supply of tissue for treatments and the immune response of the patient’s own body against the new implanted tissue/organs. To solve these issues, tissue engineering aims to develop artificial analogs derived from a patient’s own cells instead of donor tissue/organs for treatment. To this end, a promising approach, known as scaffold-based tissue engineering, is to seed engineered constructs or scaffolds with cells to form artificial analogs, which then develop with time into new tissue/organs for implantation. The mechanical properties of the scaffold play a critical role in the success of scaffold-based treatments, as the scaffold is expected to provide a temporary support for the generation of new tissue/organs without causing failure at any time during the treatment process. It is noted that due to the degradation of scaffold in the treatment process, the mechanical properties of the scaffold are not constant but change with time dynamically. This raises two scientific issues; one is the representation of the time-dependent mechanical properties and the other one is the monitoring of these properties, especially in the in vivo environments (i.e., upon the implantation of scaffolds into animal/patient bodies). To address these issues, this research is aimed at performing a novel study on the modelling and in vivo monitoring of the time dependent mechanical properties of tissue engineering scaffolds. To represent the time-dependent mechanical properties of a scaffold, a novel model based on the concept of finite element model updating is developed. The model development involves three steps: (1) development of a finite element model for the effective mechanical properties of the scaffold, (2) parametrizing the finite element model by selecting parameters associated with the scaffold microstructure and/or material properties, which vary with scaffold degradation, and (3) identifying selected parameters as functions of time based on measurements from the tests on the scaffold mechanical properties as they degrade. To validate the developed model, scaffolds were made from the biocompatible polymer polycaprolactone (PCL) mixed with hydroxyapatite (HA) nanoparticles and their mechanical properties were examined in terms of the Young modulus. Based on the bulk degradation exhibited by the PCL/HA scaffold, the molecular weight was selected for model updating. With the identified molecular weight, the finite element model v developed was effective for predicting the time-dependent mechanical properties of PCL/HA scaffolds during degradation . To monitor and characterize scaffold mechanical properties in vivo, novel methods based on synchrotron-based phase contrast imaging and finite element modeling were developed. The first method is to represent the scaffold mechanical properties from the measured deflection. In this method, the phase contrast imaging is used to characterize the scaffold deflection caused by ultrasound radiation forces; and the finite element modelling is used to represent the ultrasonic loading on the scaffold, thus predicting the mechanical properties from the measured deflection. The second method is to characterize the scaffold degradation due to surface erosion, which involves the remote sensing of the time dependent morphology of tissue scaffolds by phase contrast imaging and the estimation of time dependent mass loss of the scaffolds from the sensed morphology. The last method is to relate the elastic mechanical property and nonlinear stress-strain behavior to the scaffold geometry, both changing with time during surface erosion. To validate the above methods, scaffolds was made from varying biomaterials (PLGA and PCL) and their mechanical properties (degradation, mass loss, and elastic modulus) were examined experimentally. The results obtained illustrate the methods developed in this research are effective to monitor and characterize scaffold mechanical properties. The significance of this research is that the model developed for the scaffold mechanical properties can be used in the design of scaffolds with the desired mechanical properties, instead of the trial and error methods typical in current scaffold design; and that these novel monitoring methods based on synchrotron imaging can be used to characterize the scaffold time-dependent mechanical properties in the in vivo environments, representing an important advance in tissue engineering

Arterial Tissue Perforation Using Ultrasonically Vibrating Wire Waveguides

Chronic Total Occlusions (CTOs) are fibrous and calcified atherosclerotic lesions which completely occlude the artery. They are difficult to treat with standard dilation procedures as they cannot be traversed easily. Their treatment is also associated with a high risk of arterial perforation. Low frequency ultrasonic vibrations delivered via wire waveguides represent a minimally invasive treatment for CTOs and other tissue ablation applications. These devices typically operate at 20–50 kHz delivering wire waveguide distal tip amplitudes of vibration of 0-60 μm. The diseased tissue is ablated or disrupted by repetitive direct mechanical contact and cavitation. This research assesses the susceptibility of arterial tissue to perforation and residual damage under the action of ultrasonically energised wire waveguides. Using Finite Element Analysis (FEA), a linear acoustic model of the wire waveguide distal tips can predict the pressures for a range of operating parameters typically used for these devices. High mesh densities (140 EPW) were required to solve the entire acoustic field, including complex wave interactions. The FEA model was used to aid in the further design and modification of an ultrasonic apparatus and wire waveguide (0–34.3 μm at 22.5 kHz). Using a test rig, the effects of distal tip amplitudes of vibration, feedrate and angled entry on the perforation forces, energy and temperature were measured. The perforation forces reduced (≈ 60%, 6.13 N - 2.46 N mean) when the wire waveguide was energised at low amplitudes of vibrations (\u3c 27.8 μm). There were no significant change in tissue perforation forces above this or when the waveguide was operating above the cavitation threshold. Histological analysis also showed tissue removal. While this knowledge is useful in the prediction and avoidance of perforations during CTO operations; it is also envisaged that this information can aid in the design and development of generic ultrasonic wire waveguide tissue cutting tools

前立腺癌スクリーニングのためのロボット触診システムの開発

Tohoku University田中真美課

On Plume Dispersion after Line Source in Crossflows over Rough Surfaces

published_or_final_versio

Patient-specific simulation for autonomous surgery

An Autonomous Robotic Surgical System (ARSS) has to interact with the complex anatomical environment, which is deforming and whose properties are often uncertain. Within this context, an ARSS can benefit from the availability of patient-specific simulation of the anatomy. For example, simulation can provide a safe and controlled environment for the design, test and validation of the autonomous capabilities. Moreover, it can be used to generate large amounts of patient-specific data that can be exploited to learn models and/or tasks. The aim of this Thesis is to investigate the different ways in which simulation can support an ARSS and to propose solutions to favor its employability in robotic surgery. We first address all the phases needed to create such a simulation, from model choice in the pre-operative phase based on the available knowledge to its intra-operative update to compensate for inaccurate parametrization. We propose to rely on deep neural networks trained with synthetic data both to generate a patient-specific model and to design a strategy to update model parametrization starting directly from intra-operative sensor data. Afterwards, we test how simulation can assist the ARSS, both for task learning and during task execution. We show that simulation can be used to efficiently train approaches that require multiple interactions with the environment, compensating for the riskiness to acquire data from real surgical robotic systems. Finally, we propose a modular framework for autonomous surgery that includes deliberative functions to handle real anatomical environments with uncertain parameters. The integration of a personalized simulation proves fundamental both for optimal task planning and to enhance and monitor real execution. The contributions presented in this Thesis have the potential to introduce significant step changes in the development and actual performance of autonomous robotic surgical systems, making them closer to applicability to real clinical conditions