Six Degree-of Freedom Haptic Rendering for Dental Implantology Simulation

International audienceDental implantology procedures are among the most com- plex surgical procedures executed by dentists. During the critical part of the procedure, the jawbone is drilled at the location of the missing tooth (or the missing group of teeth). This asks for specic skills from the dentists, who need to be well trained. In this paper we present a virtual reality based training system for im- plantology and we mainly focus on the simulation of drilling. We have two main contributions: The rst one is a method for precise haptic rendering of contacts between the drilling tool and the jawbone model issued from a CT-scan. The second one is the real-time simulation of the jawbone erosion during drilling which is compatible with the haptic rendering of contacts

Modeling and rendering for development of a virtual bone surgery system

A virtual bone surgery system is developed to provide the potential of a realistic, safe, and controllable environment for surgical education. It can be used for training in orthopedic surgery, as well as for planning and rehearsal of bone surgery procedures...Using the developed system, the user can perform virtual bone surgery by simultaneously seeing bone material removal through a graphic display device, feeling the force via a haptic deice, and hearing the sound of tool-bone interaction --Abstract, page iii

Haptic Enhancement of Sensorimotor Learning for Clinical Training Applications

Modern surgical training requires radical change with the advent of increasingly complex procedures, restricted working hours, and reduced ‘hands-on’ training in the operating theatre. Moreover, an increased focus on patient safety means there is a greater need to objectively measure proficiency in trainee surgeons. Indeed, the existing evidence suggests that surgical sensorimotor skill training is not adequate for modern surgery. This calls for new training methodologies which can increase the acquisition rate of sensorimotor skill. Haptic interventions offer one exciting possible avenue for enhancing surgical skills in a safe environment. Nevertheless, the best approach for implementing novel training methodologies involving haptic intervention within existing clinical training curricula has yet to be determined. This thesis set out to address this issue. In Chapter 2, the development of two novel tools which enable the implementation of bespoke visuohaptic environments within robust experimental protocols is described. Chapters 3 and 4 report the effects of intensive, long-term training on the acquisition of a compliance discrimination skill. The results indicate that active behaviour is intrinsically linked to compliance perception, and that long-term training can help to improve the ability of detecting compliance differences. Chapter 5 explores the effects of error augmentation and parameter space exploration on the learning of a complex novel task. The results indicate that error augmentation can help improve learning rate, and that physical workspace exploration may be a driver for motor learning. This research is a first step towards the design of objective haptic intervention strategies to help support the rapid acquisition of sensorimotor skill. The work has applications in clinical settings such as surgical training, dentistry and physical rehabilitation, as well as other areas such as sport

Virtual Reality Simulation of Glenoid Reaming Procedure

Glenoid reaming is a bone machining operation in Total Shoulder Arthroplasty (TSA) in which the glenoid bone is resurfaced to make intimate contact with implant undersurface. While this step is crucial for the longevity of TSA, many surgeons find it technically challenging. With the recent advances in Virtual Reality (VR) simulations, it has become possible to realistically replicate complicated operations without any need for patients or cadavers, and at the same time, provide quantitative feedback to improve surgeons\u27 psycho-motor skills. In light of these advantages, the current thesis intends to develop tools and methods required for construction of a VR simulator for glenoid reaming, in an attempt to construct a reliable tool for preoperative training and planning for surgeons involved with TSA. Towards the end, this thesis presents computational algorithms to appropriately represent surgery tool and bone in the VR environment, determine their intersection and compute realistic haptic feedback based on the intersections. The core of the computations is constituted by sampled geometrical representations of both objects. In particular, point cloud model of the tool and voxelized model of bone - that is derived from Computed Tomography (CT) images - are employed. The thesis shows how to efficiently construct these models and adequately represent them in memory. It also elucidates how to effectively use these models to rapidly determine tool-bone collisions and account for bone removal momentarily. Furthermore, the thesis applies cadaveric experimental data to study the mechanics of glenoid reaming and proposes a realistic model for haptic computations. The proposed model integrates well with the developed computational tools, enabling real-time haptic and graphic simulation of glenoid reaming. Throughout the thesis, a particular emphasis is placed upon computational efficiency, especially on the use of parallel computing using Graphics Processing Units (GPUs). Extensive implementation results are also presented to verify the effectiveness of the developments. Not only do the results of this thesis advance the knowledge in the simulation of glenoid reaming, but they also rigorously contribute to the broader area of surgery simulation, and can serve as a step forward to the wider implementation of VR technology in surgeon training programs

Multi-finger grasps in a dynamic environment

Most current state-of-the-art haptic devices render only a single force, however almost all human grasps are characterised by multiple forces and torques applied by the fingers and palms of the hand to the object. In this chapter we will begin by considering the different types of grasp and then consider the physics of rigid objects that will be needed for correct haptic rendering. We then describe an algorithm to represent the forces associated with grasp in a natural manner. The power of the algorithm is that it considers only the capabilities of the haptic device and requires no model of the hand, thus applies to most practical grasp types. The technique is sufficiently general that it would also apply to multi-hand interactions, and hence to collaborative interactions where several people interact with the same rigid object. Key concepts in friction and rigid body dynamics are discussed and applied to the problem of rendering multiple forces to allow the person to choose their grasp on a virtual object and perceive the resulting movement via the forces in a natural way. The algorithm also generalises well to support computation of multi-body physic

Machine learning and interactive real-time simulation for training on relevant total hip replacement skills.

Virtual Reality simulators have proven to be an excellent tool in the medical sector to help trainees mastering surgical abilities by providing them with unlimited training opportunities. Total Hip Replacement (THR) is a procedure that can benefit significantly from VR/AR training, given its non-reversible nature. From all the different steps required while performing a THR, doctors agree that a correct fitting of the acetabular component of the implant has the highest relevance to ensure successful outcomes. Acetabular reaming is the step during which the acetabulum is resurfaced and prepared to receive the acetabular implant. The success of this step is directly related to the success of fitting the acetabular component. Therefore, this thesis will focus on developing digital tools that can be used to assist the training of acetabular reaming. Devices such as navigation systems and robotic arms have proven to improve the final accuracy of the procedure. However, surgeons must learn to adapt their instrument movements to be recognised by infrared cameras. When surgeons are initially introduced to these systems, surgical times can be extended up to 20 minutes, maximising surgical risks. Training opportunities are sparse, given the high investment required to purchase these devices. As a cheaper alternative, we developed an Augmented Reality (AR) alternative for training on the calibration of imageless navigation systems (INS). At the time, there were no alternative simulators that using head-mounted displays to train users into the steps to calibrate such systems. Our simulator replicates the presence of an infrared camera and its interaction with the reflecting markers located on the surgical tools. A group of 6 hip surgeons were invited to test the simulator. All of them expressed their satisfaction with the ease of use and attractiveness of the simulator as well as the similarity of interaction with the real procedure. The study confirmed that our simulator represents a cheaper and faster option to train multiple surgeons simultaneously in the use of Imageless Navigation Systems (INS) than learning exclusively on the surgical theatre. Current reviews on simulators for orthopaedic surgical procedures lack objective metrics of assessment given a standard set of design requirements. Instead, most of them rely exclusively on the level of interaction and functionality provided. We propose a comparative assessment rubric based on three different evaluation criteria. Namely immersion, interaction fidelity, and applied learning theories. After our assessment, we found that none of the simulators available for THR provides an accurate interactive representation of resurfacing procedures such as acetabular reaming based on force inputs exerted by the user. This feature is indispensable for an orthopaedics simulator, given that hand-eye coordination skills are essential skills to be trained before performing non-reversible bone removal on real patients. Based on the findings of our comparative assessment, we decided to develop a model to simulate the physically-based deformation expected during traditional acetabular reaming, given the user’s interaction with a volumetric mesh. Current interactive deformation methods on high-resolution meshes are based on geometrical collision detection and do not consider the contribution of the materials’ physical properties. By ignoring the effect of the material mechanics and the force exerted by the user, they become inadequate for training on hand- eye coordination skills transferable to the surgical theatre. Volumetric meshes are preferred in surgical simulation to geometric ones, given that they are able to represent the internal evolution of deformable solids resulting from cutting and shearing operations. Existing numerical methods for representing linear and corotational FEM cuts can only maintain interactive framerates at a low resolution of the mesh. Therefore, we decided to train a machine-learning model to learn the continuum mechanic laws relevant to acetabular reaming and predict deformations at interactive framerates. To the best of our knowledge, no research has been done previously on training a machine learning model on non-elastic FEM data to achieve results at interactive framerates. As training data, we used the results from XFEM simulations precomputed over 5000 frames for plastic deformations on tetrahedral meshes with 20406 elements each. We selected XFEM simulation as the physically-based deformation ground-truth given its accuracy and fast convergence to represent cuts, discontinuities and large strain rates. Our machine learning-based interactive model was trained following the Graph Neural Networks (GNN) blocks. GNNs were selected to learn on tetrahedral meshes as other supervised-learning architectures like the Multilayer perceptron (MLP), and Convolutional neural networks (CNN) are unable to learn the relationships between entities with an arbitrary number of neighbours. The learned simulator identifies the elements to be removed on each frame and describes the accumulated stress evolution in the whole machined piece. Using data generated from the results of XFEM allowed us to embed the effects of non-linearities in our interactive simulations without extra processing time. The trained model executed the prediction task using our tetrahedral mesh and unseen reamer orientations faster per frame than the time required to generate the training FEM dataset. Given an unseen orientation of the reamer, the trained GN model updates the value of accumulated stress on each of the 20406 tetrahedral elements that constitute our mesh during the prediction task. Once this value is updated, the tetrahedrons to be removed from the mesh are identified using a threshold condition. After using each single-frame output as input for the following prediction repeatedly for up to 60 iterations, our model can maintain an accuracy of up to 90.8% in identifying the status of each element given their value of accumulated stress. Finally, we demonstrate how the developed estimator can be easily connected to any game engine and included in developing a fully functional hip arthroplasty simulator

Design and development of a VR system for exploration of medical data using haptic rendering and high quality visualization

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Micro/Nano Manufacturing

Micro- and nano-scale manufacturing has been the subject of ever more research and industrial focus over the past 10 years. Traditional lithography-based technology forms the basis of micro-electro-mechanical systems (MEMS) manufacturing, but also precision manufacturing technologies have been developed to cover micro-scale dimensions and accuracies. Furthermore, these fundamentally different technology platforms are currently combined in order to exploit the strengths of both platforms. One example is the use of lithography-based technologies to establish nanostructures that are subsequently transferred to 3D geometries via injection molding. Manufacturing processes at the micro-scale are the key-enabling technologies to bridge the gap between the nano- and the macro-worlds to increase the accuracy of micro/nano-precision production technologies, and to integrate different dimensional scales in mass-manufacturing processes. Accordingly, this Special Issue seeks to showcase research papers, short communications, and review articles that focus on novel methodological developments in micro- and nano-scale manufacturing, i.e., on novel process chains including process optimization, quality assurance approaches and metrology