Virtual Reality Simulator for Training in Myringotomy with Tube Placement

Myringotomy refers to a surgical incision in the eardrum, and it is often followed by ventilation tube placement to treat middle-ear infections. The procedure is difficult to learn; hence, the objectives of this work were to develop a virtual-reality training simulator, assess its face and content validity, and implement quantitative performance metrics and assess construct validity. A commercial digital gaming engine (Unity3D) was used to implement the simulator with support for 3D visualization of digital ear models and support for major surgical tasks. A haptic arm co-located with the stereo scene was used to manipulate virtual surgical tools and to provide force feedback. A questionnaire was developed with 14 face validity questions focusing on realism and 6 content validity questions focusing on training potential. Twelve participants from the Department of Otolaryngology were recruited for the study. Responses to 12 of the 14 face validity questions were positive. One concern was with contact modeling related to tube insertion into the eardrum, and the second was with movement of the blade and forceps. The former could be resolved by using a higher resolution digital model for the eardrum to improve contact localization. The latter could be resolved by using a higher fidelity haptic device. With regard to content validity, 64% of the responses were positive, 21% were neutral, and 15% were negative. In the final phase of this work, automated performance metrics were programmed and a construct validity study was conducted with 11 participants: 4 senior Otolaryngology consultants and 7 junior Otolaryngology residents. Each participant performed 10 procedures on the simulator and metrics were automatically collected. Senior Otolaryngologists took significantly less time to completion compared to junior residents. Junior residents had 2.8 times more errors as compared to experienced surgeons. The senior surgeons also had significantly longer incision lengths, more accurate incision angles, and lower magnification keeping both the umbo and annulus in view. All metrics were able to discriminate senior Otolaryngologists from junior residents with a significance of p \u3c 0.002. The simulator has sufficient realism, training potential and performance discrimination ability to warrant a more resource intensive skills transference study

Real-time hybrid cutting with dynamic fluid visualization for virtual surgery

It is widely accepted that a reform in medical teaching must be made to meet today's high volume training requirements. Virtual simulation offers a potential method of providing such trainings and some current medical training simulations integrate haptic and visual feedback to enhance procedure learning. The purpose of this project is to explore the capability of Virtual Reality (VR) technology to develop a training simulator for surgical cutting and bleeding in a general surgery

Determination of critical factors for fast and accurate 2D medical image deformation

The advent of medical imaging technology enabled physicians to study patient anatomy non-invasively and revolutionized the medical community. As medical images have become digitized and the resolution of these images has increased, software has been developed to allow physicians to explore their patients\u27 image studies in an increasing number of ways by allowing viewing and exploration of reconstructed three-dimensional models. Although this has been a boon to radiologists, who specialize in interpreting medical images, few software packages exist that provide fast and intuitive interaction for other physicians. In addition, although the users of these applications can view their patient data at the time the scan was taken, the placement of the tissues during a surgical intervention is often different due to the position of the patient and methods used to provide a better view of the surgical field. None of the commonly available medical image packages allow users to predict the deformation of the patient\u27s tissues under those surgical conditions. This thesis analyzes the performance and accuracy of a less computationally intensive yet physically-based deformation algorithm- the extended ChainMail algorithm. The proposed method allows users to load DICOM images from medical image studies, interactively classify the tissues in those images according to their properties under deformation, deform the tissues in two dimensions, and visualize the result. The method was evaluated using data provided by the Truth Cube experiment, where a phantom made of material with properties similar to liver under deformation was placed under varying amounts of uniaxial strain. CT scans were before and after the deformations. The deformation was performed on a single DICOM image from the study that had been manually classified as well as on data sets generated from that original image. These generated data sets were ideally segmented versions of the phantom images that had been scaled to varying fidelities in order to evaluate the effect of image size on the algorithm\u27s accuracy and execution time. Two variations of the extended ChainMail algorithm parameters were also implemented for each of the generated data sets in order to examine the effect of the parameters. The resultant deformations were compared with the actual deformations as determined by the Truth Cube experimenters. For both variations of the algorithm parameters, the predicted deformations at 5% uniaxial strain had an RMS error of a similar order of magnitude to the errors in a finite element analysis performed by the truth cube experimenters for the deformations at 18.25% strain. The average error was able to be reduced by approximately between 10-20% for the lower fidelity data sets through the use of one of the parameter schemes, although the benefit decreased as the image size increased. When the algorithm was evaluated under 18.25% strain, the average errors were more than 8 y times that of the errors in the finite element analysis. Qualitative analysis of the deformed images indicated differing degrees of accuracy across the ideal image set, with the largest displacements estimated closer to the initial point of deformation. This is hypothesized to be a result of the order in which deformation was processed for points in the image. The algorithm execution time was examined for the varying generated image fidelities. For a generated image that was approximately 18.5% of the size of the tissue in the original image, the execution time was less than 15 seconds. In comparison, the algorithm processing time for the full-scale image was over 3 y hours. The analysis of the extended ChainMail algorithm for use in medical image deformation emphasizes the importance of the choice of algorithm parameters on the accuracy of the deformations and of data set size on the processing time

Instrument-tissue segment interaction using finite element modeling

Los Alamitos, C

Realistic tool-tissue interaction models for surgical simulation and planning

Surgical simulators present a safe and potentially effective method for surgical training, and can also be used in pre- and intra-operative surgical planning. Realistic modeling of medical interventions involving tool-tissue interactions has been considered to be a key requirement in the development of high-fidelity simulators and planners. The soft-tissue constitutive laws, organ geometry and boundary conditions imposed by the connective tissues surrounding the organ, and the shape of the surgical tool interacting with the organ are some of the factors that govern the accuracy of medical intervention planning.\ud \ud This thesis is divided into three parts. First, we compare the accuracy of linear and nonlinear constitutive laws for tissue. An important consequence of nonlinear models is the Poynting effect, in which shearing of tissue results in normal force; this effect is not seen in a linear elastic model. The magnitude of the normal force for myocardial tissue is shown to be larger than the human contact force discrimination threshold. Further, in order to investigate and quantify the role of the Poynting effect on material discrimination, we perform a multidimensional scaling study. Second, we consider the effects of organ geometry and boundary constraints in needle path planning. Using medical images and tissue mechanical properties, we develop a model of the prostate and surrounding organs. We show that, for needle procedures such as biopsy or brachytherapy, organ geometry and boundary constraints have more impact on target motion than tissue material parameters. Finally, we investigate the effects surgical tool shape on the accuracy of medical intervention planning. We consider the specific case of robotic needle steering, in which asymmetry of a bevel-tip needle results in the needle naturally bending when it is inserted into soft tissue. We present an analytical and finite element (FE) model for the loads developed at the bevel tip during needle-tissue interaction. The analytical model explains trends observed in the experiments. We incorporated physical parameters (rupture toughness and nonlinear material elasticity) into the FE model that included both contact and cohesive zone models to simulate tissue cleavage. The model shows that the tip forces are sensitive to the rupture toughness. In order to model the mechanics of deflection of the needle, we use an energy-based formulation that incorporates tissue-specific parameters such as rupture toughness, nonlinear material elasticity, and interaction stiffness, and needle geometric and material properties. Simulation results follow similar trends (deflection and radius of curvature) to those observed in macroscopic experimental studies of a robot-driven needle interacting with gels

Virtual reality training and assessment in laparoscopic rectum surgery

Background: Virtual-reality (VR) based simulation techniques offer an efficient and low cost alternative to conventional surgery training. This article describes a VR training and assessment system in laparoscopic rectum surgery. Methods: To give a realistic visual performance of interaction between membrane tissue and surgery tools, a generalized cylinder based collision detection and a multi-layer mass-spring model are presented. A dynamic assessment model is also designed for hierarchy training evaluation. Results: With this simulator, trainees can operate on the virtual rectum with both visual and haptic sensation feedback simultaneously. The system also offers surgeons instructions in real time when improper manipulation happens. The simulator has been tested and evaluated by ten subjects. Conclusions: This prototype system has been verified by colorectal surgeons through a pilot study. They believe the visual performance and the tactile feedback are realistic. It exhibits the potential to effectively improve the surgical skills of trainee surgeons and significantly shorten their learning curve. © 2014 John Wiley & Sons, Ltd

Research on real-time physics-based deformation for haptic-enabled medical simulation

This study developed a multiple effective visuo-haptic surgical engine to handle a variety of surgical manipulations in real-time. Soft tissue models are based on biomechanical experiment and continuum mechanics for greater accuracy. Such models will increase the realism of future training systems and the VR/AR/MR implementations for the operating room

Towards retrieving force feedback in robotic-assisted surgery: a supervised neuro-recurrent-vision approach

Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure. To overcome this limitation, we propose a novel force estimation approach that combines a vision based solution with supervised learning to estimate the applied force and provide the surgeon with a suitable representation of it. The proposed solution starts with extracting the geometry of motion of the heart's surface by minimizing an energy functional to recover its 3D deformable structure. A deep network, based on a LSTM-RNN architecture, is then used to learn the relationship between the extracted visual-geometric information and the applied force, and to find accurate mapping between the two. Our proposed force estimation solution avoids the drawbacks usually associated with force sensing devices, such as biocompatibility and integration issues. We evaluate our approach on phantom and realistic tissues in which we report an average root-mean square error of 0.02 N.Peer ReviewedPostprint (author's final draft

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

Modelling and simulation of flexible instruments for minimally invasive surgical training in virtual reality

Improvements in quality and safety standards in surgical training, reduction in training hours and constant technological advances have challenged the traditional apprenticeship model to create a competent surgeon in a patient-safe way. As a result, pressure on training outside the operating room has increased. Interactive, computer based Virtual Reality (VR) simulators offer a safe, cost-effective, controllable and configurable training environment free from ethical and patient safety issues. Two prototype, yet fully-functional VR simulator systems for minimally invasive procedures relying on flexible instruments were developed and validated. NOViSE is the first force-feedback enabled VR simulator for Natural Orifice Transluminal Endoscopic Surgery (NOTES) training supporting a flexible endoscope. VCSim3 is a VR simulator for cardiovascular interventions using catheters and guidewires. The underlying mathematical model of flexible instruments in both simulator prototypes is based on an established theoretical framework – the Cosserat Theory of Elastic Rods. The efficient implementation of the Cosserat Rod model allows for an accurate, real-time simulation of instruments at haptic-interactive rates on an off-the-shelf computer. The behaviour of the virtual tools and its computational performance was evaluated using quantitative and qualitative measures. The instruments exhibited near sub-millimetre accuracy compared to their real counterparts. The proposed GPU implementation further accelerated their simulation performance by approximately an order of magnitude. The realism of the simulators was assessed by face, content and, in the case of NOViSE, construct validity studies. The results indicate good overall face and content validity of both simulators and of virtual instruments. NOViSE also demonstrated early signs of construct validity. VR simulation of flexible instruments in NOViSE and VCSim3 can contribute to surgical training and improve the educational experience without putting patients at risk, raising ethical issues or requiring expensive animal or cadaver facilities. Moreover, in the context of an innovative and experimental technique such as NOTES, NOViSE could potentially facilitate its development and contribute to its popularization by keeping practitioners up to date with this new minimally invasive technique.Open Acces