Visualization of medical data in three-dimensional (3D) or two-dimensional (2D) views is a complex area of research. In many fields 3D views are used to understand the shape of an object, and 2D views are used to understand spatial relationships. It is unclear how 2D/3D views play a role in the medical field. Using 3D views can potentially decrease the learning curve experienced with traditional 2D views by providing a whole representation of the patient's anatomy. However, there are challenges with 3D views compared with 2D. This current study expands on a previous study to evaluate the mental workload associated with both 2D and 3D views. Twenty-five first-year medical students were asked to localize three anatomical structures--gallbladder, celiac trunk, and superior mesenteric artery--in either 2D or 3D environments. Accuracy and time were taken as the objective measures for mental workload. The NASA Task Load Index (NASA-TLX) was used as a subjective measure for mental workload. Results showed that participants viewing in 3D had higher localization accuracy and a lower subjective measure of mental workload, specifically, the mental demand component of the NASA-TLX. Results from this study may prove useful for designing curricula in anatomy education and improving training procedures for surgeons.
Military operations are turning to more complex and advanced automation technology for minimum risk and maximum efficiency. A critical piece to this strategy is unmanned aerial vehicles (UAVs). UAVs require the intelligence to safely maneuver along a path to an intended target, avoiding obstacles such as other aircrafts or enemy threats. Often automated path planning algorithms are employed to specify targets for a UAV to fly to. To date, path-planning algorithms have been limited to two-dimensional problem formulations. This paper presents a unique three-dimensional path planning problem formulation and solution approach using Particle Swarm Optimization (PSO). The problem formulation was designed to minimize risk due to enemy threats while simultaneously minimizing fuel consumption. The initial design point is a straight path between the current position and the desired target. Using PSO, an optimized path is generated through B-spline curves. The resulting paths can be optimized with a preference towards maximum safety, minimum fuel consumption or a combination of the two. The problem formulation and solution implementation is described along with the results from several simulated scenarios. Military operations are turning to more complex and advanced automation technology for minimum risk and maximum efficiency. A critical piece to this strategy is unmanned aerial vehicles (UAVs). UAVs require the intelligence to safely maneuver along a path to an intended target, avoiding obstacles such as other aircrafts or enemy threats. Often automated path planning algorithms are employed to specify targets for a UAV to fly to. To date, path-planning algorithms have been limited to two-dimensional problem formulations. This paper presents a unique three-dimensional path planning problem formulation and solution approach using Particle Swarm Optimization (PSO). The problem formulation was designed to minimize risk due to enemy threats while simultaneously minimizing fuel consumption. The initial design point is a straight path between the current position and the desired target. Using PSO, an optimized path is generated through B-spline curves. The resulting paths can be optimized with a preference towards maximum safety, minimum fuel consumption or a combination of the two. The problem formulation and solution implementation is described along with the results from several simulated scenarios. Keywords Virtual Reality Applications Center
The visualization of medical images obtained from scanning techniques such as computed tomography and magnetic resonance imaging is a well-researched field. However, advanced tools and methods to manipulate these data for surgical planning and other tasks have not seen widespread use among medical professionals. Radiologists have begun using more advanced visualization packages on desktop computer systems, but most physicians continue to work with basic two-dimensional grayscale images or not work directly with the data at all. In addition, new display technologies that are in use in other fields have yet to be fully applied in medicine. It is our estimation that usability is the key aspect in keeping this new technology from being more widely used by the medical community at large. Therefore, we have a software and hardware framework that not only make use of advanced visualization techniques, but also feature powerful, yet simple-to-use, interfaces. A virtual reality system was created to display volume-rendered medical models in three dimensions. It was designed to run in many configurations, from a large cluster of machines powering a multiwalled display down to a single desktop computer. An augmented reality system was also created for, literally, hands-on interaction when viewing models of medical data. Last, a desktop application was designed to provide a simple visualization tool, which can be run on nearly any computer at a user's disposal. This research is directed toward improving the capabilities of medical professionals in the tasks of preoperative planning, surgical training, diagnostic assistance, and patient education.
Military operations are turning to more complex and advanced automation technology for minimum risk and maximum efficiency. A critical piece to this strategy is unmanned aerial vehicles (UAVs). UAVs require the intelligence to safely maneuver along a path to an intended target, avoiding obstacles such as other aircrafts or enemy threats. Often automated path planning algorithms are employed to specify targets for a UAV to fly to. To date, path-planning algorithms have been limited to two-dimensional problem formulations. This paper presents a unique three-dimensional path planning problem formulation and solution approach using Particle Swarm Optimization (PSO). The problem formulation was designed to minimize risk due to enemy threats and also to minimize fuel consumption incurred by deviating from the original path. In addition, a third objective in the problem formulation takes into account reconnaissance targets. The initial design point is defined as the original path of the UAV. Using PSO, alternate paths are generated using B-spline curves, optimized based on preferences set for the three objectives. The resulting paths can be optimized with a preference towards maximum safety, minimum fuel consumption, or target reconnaissance. The problem formulation and solution implementation is described along with the results from several simulated scenarios.
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