Proxy chain method and its application to scientific visualization

Journal ArticleWe present a method for combining multiple point-based constraints in haptic programming environments. Instead of using a single proxy point for haptic feedback, the method maintains a separate proxy for each constraint. The reaction force is computed by linking the proxies in a chain. Constraints are applied in sequential order, such that the proxy found in the current step becomes the probe for the next step in the chain. The advantage of the method over previous approaches is that the constraints are maintained precisely and the output is well-defined. We illustrate the method with examples from the domain of 3D scientific data visualization. Finally, we present the results of an experiment conducted to quantify the contribution of haptic guidance in two representative vector field exploration tasks

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

Robot-assistive minimally invasive surgery:trends and future directions

The evolution of medical technologies—such as surgical devices and imaging techniques—has transformed all aspects of surgery. A key area of development is robot-assisted minimally invasive surgery (MIS). This review paper provides an overview of the evolution of robotic MIS, from its infancy to our days, and envisioned future challenges. It provides an outlook of breakthrough surgical robotic platforms, their clinical applications, and their evolution over the years. It discusses how the integration of robotic, imaging, and sensing technologies has contributed to create novel surgical platforms that can provide the surgeons with enhanced dexterity, precision, and surgical navigation while reducing the invasiveness and efficacy of the intervention. Finally, this review provides an outlook on the future of robotic MIS discussing opportunities and challenges that the scientific community will have to address in the coming decade. We hope that this review serves to provide a quick and accessible way to introduce the readers to this exciting and fast-evolving area of research, and to inspire future research in this field

Multicamera 3D Viewpoint Adjustment for Robotic Surgery via Deep Reinforcement Learning

While robot-assisted minimally invasive surgery (RMIS) procedures afford a variety of benefits over open surgery and manual laparoscopic operations (including increased tool dexterity, reduced patient pain, incision size, trauma and recovery time, and lower infection rates [1], lack of spatial awareness remains an issue. Typical laparoscopic imaging can lack sufficient depth cues and haptic feedback, if provided, rarely reflects realistic tissue-tool interactions. This work is part of a larger ongoing research effort to reconstruct 3D surfaces using multiple viewpoints in RMIS to increase visual perception. The manual placement and adjustment of multicamera systems in RMIS are nonideal and prone to error [2], and other autonomous approaches focus on tool tracking and do not consider reconstruction of the surgical scene [3,4,5]. The group\u27s previous work investigated a novel, context-aware autonomous camera positioning method [6], which incorporated both tool location and scene coverage for multiple camera viewpoint adjustments. In this paper, the authors expand upon this prior work by implementing a streamlined deep reinforcement learning approach between optimal viewpoints calculated using the prior method [6] which encourages discovery of otherwise unobserved and additional camera viewpoints. Combining the framework and robustness of the previous work with the efficiency and additional viewpoints of the augmentations presented here results in improved performance and scene coverage promising towards real-time implementation

Visual Perception and Cognition in Image-Guided Intervention

Surgical image visualization and interaction systems can dramatically affect the efficacy and efficiency of surgical training, planning, and interventions. This is even more profound in the case of minimally-invasive surgery where restricted access to the operative field in conjunction with limited field of view necessitate a visualization medium to provide patient-specific information at any given moment. Unfortunately, little research has been devoted to studying human factors associated with medical image displays and the need for a robust, intuitive visualization and interaction interfaces has remained largely unfulfilled to this day. Failure to engineer efficient medical solutions and design intuitive visualization interfaces is argued to be one of the major barriers to the meaningful transfer of innovative technology to the operating room. This thesis was, therefore, motivated by the need to study various cognitive and perceptual aspects of human factors in surgical image visualization systems, to increase the efficiency and effectiveness of medical interfaces, and ultimately to improve patient outcomes. To this end, we chose four different minimally-invasive interventions in the realm of surgical training, planning, training for planning, and navigation: The first chapter involves the use of stereoendoscopes to reduce morbidity in endoscopic third ventriculostomy. The results of this study suggest that, compared with conventional endoscopes, the detection of the basilar artery on the surface of the third ventricle can be facilitated with the use of stereoendoscopes, increasing the safety of targeting in third ventriculostomy procedures. In the second chapter, a contour enhancement technique is described to improve preoperative planning of arteriovenous malformation interventions. The proposed method, particularly when combined with stereopsis, is shown to increase the speed and accuracy of understanding the spatial relationship between vascular structures. In the third chapter, an augmented-reality system is proposed to facilitate the training of planning brain tumour resection. The results of our user study indicate that the proposed system improves subjects\u27 performance, particularly novices\u27, in formulating the optimal point of entry and surgical path independent of the sensorimotor tasks performed. In the last chapter, the role of fully-immersive simulation environments on the surgeons\u27 non-technical skills to perform vertebroplasty procedure is investigated. Our results suggest that while training surgeons may increase their technical skills, the introduction of crisis scenarios significantly disturbs the performance, emphasizing the need of realistic simulation environments as part of training curriculum

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

The State of the Art of Spatial Interfaces for 3D Visualization

International audienceWe survey the state of the art of spatial interfaces for 3D visualization. Interaction techniques are crucial to data visualization processes and the visualization research community has been calling for more research on interaction for years. Yet, research papers focusing on interaction techniques, in particular for 3D visualization purposes, are not always published in visualization venues, sometimes making it challenging to synthesize the latest interaction and visualization results. We therefore introduce a taxonomy of interaction technique for 3D visualization. The taxonomy is organized along two axes: the primary source of input on the one hand and the visualization task they support on the other hand. Surveying the state of the art allows us to highlight specific challenges and missed opportunities for research in 3D visualization. In particular, we call for additional research in: (1) controlling 3D visualization widgets to help scientists better understand their data, (2) 3D interaction techniques for dissemination, which are under-explored yet show great promise for helping museum and science centers in their mission to share recent knowledge, and (3) developing new measures that move beyond traditional time and errors metrics for evaluating visualizations that include spatial interaction

Performance Factors in Neurosurgical Simulation and Augmented Reality Image Guidance

Virtual reality surgical simulators have seen widespread adoption in an effort to provide safe, cost-effective and realistic practice of surgical skills. However, the majority of these simulators focus on training low-level technical skills, providing only prototypical surgical cases. For many complex procedures, this approach is deficient in representing anatomical variations that present clinically, failing to challenge users’ higher-level cognitive skills important for navigation and targeting. Surgical simulators offer the means to not only simulate any case conceivable, but to test novel approaches and examine factors that influence performance. Unfortunately, there is a void in the literature surrounding these questions. This thesis was motivated by the need to expand the role of surgical simulators to provide users with clinically relevant scenarios and evaluate human performance in relation to image guidance technologies, patient-specific anatomy, and cognitive abilities. To this end, various tools and methodologies were developed to examine cognitive abilities and knowledge, simulate procedures, and guide complex interventions all within a neurosurgical context. The first chapter provides an introduction to the material. The second chapter describes the development and evaluation of a virtual anatomical training and examination tool. The results suggest that learning occurs and that spatial reasoning ability is an important performance predictor, but subordinate to anatomical knowledge. The third chapter outlines development of automation tools to enable efficient simulation studies and data management. In the fourth chapter, subjects perform abstract targeting tasks on ellipsoid targets with and without augmented reality guidance. While the guidance tool improved accuracy, performance with the tool was strongly tied to target depth estimation – an important consideration for implementation and training with similar guidance tools. In the fifth chapter, neurosurgically experienced subjects were recruited to perform simulated ventriculostomies. Results showed anatomical variations influence performance and could impact outcome. Augmented reality guidance showed no marked improvement in performance, but exhibited a mild learning curve, indicating that additional training may be warranted. The final chapter summarizes the work presented. Our results and novel evaluative methodologies lay the groundwork for further investigation into simulators as versatile research tools to explore performance factors in simulated surgical procedures

Robots and tools for remodeling bone

The field of robotic surgery has progressed from small teams of researchers repurposing industrial robots, to a competitive and highly innovative subsection of the medical device industry. Surgical robots allow surgeons to perform tasks with greater ease, accuracy, or safety, and fall under one of four levels of autonomy; active, semi-active, passive, and remote manipulator. The increased accuracy afforded by surgical robots has allowed for cementless hip arthroplasty, improved postoperative alignment following knee arthroplasty, and reduced duration of intraoperative fluoroscopy among other benefits. Cutting of bone has historically used tools such as hand saws and drills, with other elaborate cutting tools now used routinely to remodel bone. Improvements in cutting accuracy and additional options for safety and monitoring during surgery give robotic surgeries some advantages over conventional techniques. This article aims to provide an overview of current robots and tools with a common target tissue of bone, proposes a new process for defining the level of autonomy for a surgical robot, and examines future directions in robotic surgery

A Haptic Surface Robot Interface for Large-Format Touchscreen Displays

This thesis presents the design for a novel haptic interface for large-format touchscreens. Techniques such as electrovibration, ultrasonic vibration, and external braked devices have been developed by other researchers to deliver haptic feedback to touchscreen users. However, these methods do not address the need for spatial constraints that only restrict user motion in the direction of the constraint. This technology gap contributes to the lack of haptic technology available for touchscreen-based upper-limb rehabilitation, despite the prevalent use of haptics in other forms of robotic rehabilitation. The goal of this thesis is to display kinesthetic haptic constraints to the touchscreen user in the form of boundaries and paths, which assist or challenge the user in interacting with the touchscreen. The presented prototype accomplishes this by steering a single wheel in contact with the display while remaining driven by the user. It employs a novel embedded force sensor, which it uses to measure the interaction force between the user and the touchscreen. The haptic response of the device is controlled using this force data to characterize user intent. The prototype can operate in a simulated free mode as well as simulate rigid and compliant obstacles and path constraints. A data architecture has been created to allow the prototype to be used as a peripheral add-on device which reacts to haptic environments created and modified on the touchscreen. The long-term goal of this work is to create a haptic system that enables a touchscreen-based rehabilitation platform for people with upper limb impairments