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Register a new userConceptual Design and Preliminary Results of a VR-based Radiation Safety Training System for Interventional Radiologists
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Recent studies have reported an increased risk of developing brain and neck tumors, as well as cataracts, in practitioners in interventional radiology (IR). Occupational radiation protection in IR has been a top concern for regulatory agencies and professional societies. To help minimize occupational radiation exposure in IR, we conceptualized a virtual reality (VR) based radiation safety training system to help operators understand complex radiation fields and to avoid high radiation areas through game-like interactive simulations. The preliminary development of the system has yielded results suggesting that the training system can calculate and report the radiation exposure after each training session based on a database precalculated from computational phantoms and Monte Carlo simulations and the position information provided in real-time by the MS Hololens headset worn by trainee. In addition, real-time dose rate and cumulative dose will be displayed to the trainee by MS Hololens to help them adjust their practice. This paper presents the conceptual design of the overall hardware and software design, as well as preliminary results to combine MS HoloLens headset and complex 3D X-ray field spatial distribution data to create a mixed reality environment for safety training purpose in IR.
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Objective: Interventional MRI (i-MRI) is crucial for MR image-guided therapy. Current image reconstruction methods for dynamic MR imaging are mostly retrospective that may not be suitable for i-MRI in real-time. Therefore, an algorithm to reconstruct images without a temporal pattern as in dynamic imaging is needed for i-MRI. Methods: We proposed a low-rank and sparsity (LS) decomposition algorithm with framelet transform to reconstruct the interventional feature with a high temporal resolution. Different from the existing LS based algorithm, we utilized the spatial sparsity of both the low-rank and sparsity components. We also used a primal dual fixed point (PDFP) method for optimization of the objective function to avoid solving sub-problems. Intervention experiments with gelatin and brain phantoms were carried out for validation. Results: The LS decomposition with framelet transform and PDFP could provide the best reconstruction performance compared with those without. Satisfying reconstruction results were obtained with only 10 radial spokes for a temporal resolution of 60 ms. Conclusion and Significance: The proposed method has the potential for i-MRI in many different application scenarios.
The primary purpose of this paper is to provide a design of a blockchain-based system, which produces a verifiable record of achievements. Such a system has a wide range of potential benefits for students, employers and higher education institutions. A verifiable record of achievements enables students to present academic accomplishments to employers, within a trusted framework. Furthermore, the availability of such a record system would enable students to review their learning throughout their career, giving them a platform on which to plan for their future accomplishments, both individually and with support from other parties (for example, academic advisors, supervisors, or potential employers). The proposed system will help students in universities to increase their extra-curricular activities and improve non-academic skills. Moreover, the system will facilitate communication between industry, students, and universities for employment purposes and simplify the search for the most appropriate potential employees for the job.
In this paper we report on a study conducted with a group of older adults in which they engaged in participatory design workshops to create a VR ATM training simulation. Based on observation, recordings and the developed VR application we present the results of the workshops and offer considerations and recommendations for organizing opportunities for end users, in this case older adults, to directly engage in co-creation of cutting-edge ICT solutions. These include co-designing interfaces and interaction schemes for emerging technologies like VR and AR. We discuss such aspects as user engagement and hardware and software tools suitable for participatory prototyping of VR applications. Finally, we present ideas for further research in the area of VR participatory prototyping with users of various proficiency levels, taking steps towards developing a unified framework for co-design in AR and VR.
This paper presents an analytical design of an ultrasonic power transfer system based on piezoelectric micro-machined ultrasonic transducer (PMUT) for fully wireless brain implants in mice. The key steps like the material selection of each layer and the top electrode radius to maximize the coupling factor are well-detailed. This approach results in the design of a single cell with a high effective coupling coefficient. Furthermore, compact models are used to make the design process less time-consuming for designers. These models are based on the equivalent circuit theory for the PMUT. A cell of 107 um in radius, 5 um in thickness of Lead Zirconate Titanium (PZT), and 10 um in thickness of silicon (Si) is found to have a 4% of effective coupling coefficient among the highest values for a clamped edge boundary conditions. Simulation results show a frequency of 2.84 MHz as resonance. In case of an array, mutual impedance and numerical modeling are used to estimate the distance between the adjacent cells. In addition, the area of the proposed transducer and the number of cells are computed with the Rayleigh distance and neglecting the cross-talk among cells, respectively. The designed transducer consists of 7x7 cells in an area of 3.24 mm2. The transducer is able to deliver an acoustic intensity of 7.185 mW/mm2 for a voltage of 19.5 V for powering brain implants seated in the motor cortex and striatum of the mices brain. The maximum acoustic intensity occurs at a distance of 2.5 mm in the near field which was estimated with the Rayleigh length equation.
The Covid-19 pandemic has strained the hospital systems in many countries in the world, especially in developing countries. In many low-resource hospitals, severely ill hypoxemic Covid-19 patients are treated with various forms of low-flow oxygen therapy (0-15 L/min), including interfaces such as a nasal cannula, Hudson mask, venturi-mask, and non-rebreather masks. When 15L/min of pure oxygen flow is not sufficient for the patient, treatment guidelines suggest non-invasive positive pressure ventilation (NIPPV) or high-flow nasal oxygenation (HFNO) as the next stage of treatment. However, administering HFNO in the general wards of a low-resource hospital is difficult due to several factors, including difficulty in operation, unavailability of electric power outlets, and frequent maintenance. Therefore, in many cases, the highest level of care a patient receives in the general ward is 15L/min of oxygen on a Non-Rebreather Mask. With a shortage of Intensive Care Unit (ICU) beds, this is a major problem since intermediate forms of treatments are simply not available at an affordable cost. To address this gap, we have developed a low-cost CPAP system specifically designed for low-resource hospitals. The device is a precision venturi-based flow-generator capable of providing up to 60L/min of flow. The device utilizes the mechanics of a jet pump driven by high-pressure oxygen to increase the volumetric flow rate by entraining atmospheric air. The fraction of inspired oxygen (FiO2) can be attained between 40 - 100% using a dual-flowmeter. Consisting of a traditional 22mm breathing circuit, a non-vented CPAP mask, and a Positive End-Expiratory Pressure (PEEP) valve, the CPAP can provide positive pressures between 5-20 cm H2O. The device is manufactured using local 3D printing and workshop facilities.
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