Augmented reality (AR) for surgical robotic and autonomous systems: State of the art, challenges, and solutions

Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human-robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future

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

Haptic Interaction with 3D oriented point clouds on the GPU

Real-time point-based rendering and interaction with virtual objects is gaining popularity and importance as di�erent haptic devices and technologies increasingly provide the basis for realistic interaction. Haptic Interaction is being used for a wide range of applications such as medical training, remote robot operators, tactile displays and video games. Virtual object visualization and interaction using haptic devices is the main focus; this process involves several steps such as: Data Acquisition, Graphic Rendering, Haptic Interaction and Data Modi�cation. This work presents a framework for Haptic Interaction using the GPU as a hardware accelerator, and includes an approach for enabling the modi�cation of data during interaction. The results demonstrate the limits and capabilities of these techniques in the context of volume rendering for haptic applications. Also, the use of dynamic parallelism as a technique to scale the number of threads needed from the accelerator according to the interaction requirements is studied allowing the editing of data sets of up to one million points at interactive haptic frame rates

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

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Sensory Communication

Contains table of contents for Section 2, an introduction and reports on fourteen research projects.National Institutes of Health Grant RO1 DC00117National Institutes of Health Grant RO1 DC02032National Institutes of Health/National Institute on Deafness and Other Communication Disorders Grant R01 DC00126National Institutes of Health Grant R01 DC00270National Institutes of Health Contract N01 DC52107U.S. Navy - Office of Naval Research/Naval Air Warfare Center Contract N61339-95-K-0014U.S. Navy - Office of Naval Research/Naval Air Warfare Center Contract N61339-96-K-0003U.S. Navy - Office of Naval Research Grant N00014-96-1-0379U.S. Air Force - Office of Scientific Research Grant F49620-95-1-0176U.S. Air Force - Office of Scientific Research Grant F49620-96-1-0202U.S. Navy - Office of Naval Research Subcontract 40167U.S. Navy - Office of Naval Research/Naval Air Warfare Center Contract N61339-96-K-0002National Institutes of Health Grant R01-NS33778U.S. Navy - Office of Naval Research Grant N00014-92-J-184

物理/バーチャル空間の接続と分離を媒介する可動壁に関する研究

Tohoku University博士（情報科学）thesi

Medical Robotics

The first generation of surgical robots are already being installed in a number of operating rooms around the world. Robotics is being introduced to medicine because it allows for unprecedented control and precision of surgical instruments in minimally invasive procedures. So far, robots have been used to position an endoscope, perform gallbladder surgery and correct gastroesophogeal reflux and heartburn. The ultimate goal of the robotic surgery field is to design a robot that can be used to perform closed-chest, beating-heart surgery. The use of robotics in surgery will expand over the next decades without any doubt. Minimally Invasive Surgery (MIS) is a revolutionary approach in surgery. In MIS, the operation is performed with instruments and viewing equipment inserted into the body through small incisions created by the surgeon, in contrast to open surgery with large incisions. This minimizes surgical trauma and damage to healthy tissue, resulting in shorter patient recovery time. The aim of this book is to provide an overview of the state-of-art, to present new ideas, original results and practical experiences in this expanding area. Nevertheless, many chapters in the book concern advanced research on this growing area. The book provides critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies. This book is certainly a small sample of the research activity on Medical Robotics going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects, whether they are currently “medical roboticists” or not

3D oceanographic data compression using 3D-ODETLAP

This paper describes a 3D environmental data compression technique for oceanographic datasets. With proper point selection, our method approximates uncompressed marine data using an over-determined system of linear equations based on, but essentially different from, the Laplacian partial differential equation. Then this approximation is refined via an error metric. These two steps work alternatively until a predefined satisfying approximation is found. Using several different datasets and metrics, we demonstrate that our method has an excellent compression ratio. To further evaluate our method, we compare it with 3D-SPIHT. 3D-ODETLAP averages 20% better compression than 3D-SPIHT on our eight test datasets, from World Ocean Atlas 2005. Our method provides up to approximately six times better compression on datasets with relatively small variance. Meanwhile, with the same approximate mean error, we demonstrate a significantly smaller maximum error compared to 3D-SPIHT and provide a feature to keep the maximum error under a user-defined limit

Sensory Communication

Contains table of contents for Section 2, an introduction and reports on fifteen research projects.National Institutes of Health Grant RO1 DC00117National Institutes of Health Grant RO1 DC02032National Institutes of Health Contract P01-DC00361National Institutes of Health Contract N01-DC22402National Institutes of Health/National Institute on Deafness and Other Communication Disorders Grant 2 R01 DC00126National Institutes of Health Grant 2 R01 DC00270National Institutes of Health Contract N01 DC-5-2107National Institutes of Health Grant 2 R01 DC00100U.S. Navy - Office of Naval Research/Naval Air Warfare Center Contract N61339-94-C-0087U.S. Navy - Office of Naval Research/Naval Air Warfare Center Contract N61339-95-K-0014U.S. Navy - Office of Naval Research/Naval Air Warfare Center Grant N00014-93-1-1399U.S. Navy - Office of Naval Research/Naval Air Warfare Center Grant N00014-94-1-1079U.S. Navy - Office of Naval Research Subcontract 40167U.S. Navy - Office of Naval Research Grant N00014-92-J-1814National Institutes of Health Grant R01-NS33778U.S. Navy - Office of Naval Research Grant N00014-88-K-0604National Aeronautics and Space Administration Grant NCC 2-771U.S. Air Force - Office of Scientific Research Grant F49620-94-1-0236U.S. Air Force - Office of Scientific Research Agreement with Brandeis Universit