Radiation monitoring systems able to accurately track the radiation dose received by the patient and the medical staff during interventional fluoroscopy can be used to minimize the likelihood and severity of radiation-induced skin injuries and estimate the accumulated organ doses. We describe a method to monitor doses in real time using automatic sensors in the imaging room and a GPU-accelerated computer simulator. The Monte Carlo simulation code MC-GPU is used to estimate patient and staff doses due to primary and scattered radiation, along with the associated statistical uncertainties. The geometrical configuration of the irradiation is automatically determined and updated using data from a depth camera that tracks the location and posture of each person in the imaging room. A virtual x-ray source graphical interface is used to manually trigger the simulations. The implemented computational framework separates the simulation of the x-ray transport through the patient and the operator bodies into two coupled, sequential simulations. The initial simulation uses the patient anatomy and a c-arm source model with a collimated cone beam emitted from a point focal spot. During this simulation a large phase space file with the energy, position and direction of x rays scattered in the direction of the operator is created. The phase space file is then used as the input radiation source for the following simulation with the operator anatomy model. Particle recycling is employed as a variance reduction technique to maximize the information obtained from the limited number of particles scattered towards the operator. For a typical image acquisition, a patient skin dose map can be displayed at the operator's monitor within 10 seconds with a peak skin dose error below 1%. This work demonstrates that a dose monitoring system based on accurate Monte Carlo simulations can be used to estimate in real-time the average and peak organ doses for both the patient and the staff in interventional fluoroscopy, and provide timely information regarding possible overdoses while the imaging procedure is being performed.
Handheld devices such as smartphones and tablets are becoming useful in the medical field, as they allow physicians, radiologists, and researchers to analyze images with the benefit of mobile accessibility. However, for handheld devices to be effective, the display must be able to perform well in a wide range of ambient illumination conditions. We conducted visual experiments to quantify user performance for testing the image quality of two current-generation devices in different ambient illumination conditions while measuring ambient light levels with a real-time illuminance meter. We found and quantified that due to the high reflectivity of handheld devices, performance deteriorates as the user moves from dark areas into environments of greater ambient illumination. The quantitative analysis suggests that differences in display reflection coefficients do not affect the low illumination performance of the device but rather the performance at higher levels of illumination.
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