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In clinical X-ray imaging, a focused grid is used to enhance image contrast by removing scattered radiation. The focused grid removes scattered radiation only when the X-ray source is located in the focal point of the grid. In oblique imaging, the grid cuts off some of the primary radiation because the X-ray radiation is not aligned with the lead strips. If the tilting angle of the grid elements and the direction of X-ray radiation are not aligned, it results in less X-ray reaching the imaging detector. Therefore, in oblique imaging cases, the exposure condition is generally increased to obtain a clinically valuable image. In this paper, we propose a method to acquire proper image quality in terms of both contrast and signal-to-noise ratio (SNR) in oblique X-ray imaging by positioning the subject in a specific grid region where the X-ray projection is aligned with the tilted angle of the grid lead strips. We examined experimental imaging with several types of radiographic phantoms, and the results demonstrate the feasibility of the proposed method. This study is applicable to non-standard X-ray imaging cases involving patients who are difficult to position correctly in oblique views. In such cases, the proposed approach sets the direction of X-ray radiation without requiring patient movement. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) plays a very important role in a medical domain, such as image guided diagnostics and therapeutics. In particular, high resolution brain MRI has a great potential for preclinical and clinical procedures, because it is non-invasive imaging and shows a high level of anatomical detail. However, the high resolution MRI faces a number of challenges, such as long scan time, high magnetic field strength, and low signal to noise ratio. To solve these issues, deep learning based super-resolution networks, which provide high performance in various fields, can be employed in MRI. Since the super-resolution networks have been mainly developed to reconstruct high quality color images by using many parameters, they cannot be directly applied into MR scanners. Hence, this paper evaluates conventional networks with brain MR images, and then proposes a lightweight network employing 3D convolution, which consists of extraction, compression, and reconstruction parts. Experimental results show that the proposed network is very efficient, in terms of reconstruction quality and network complexity. [ABSTRACT FROM AUTHOR]

In the current study, we sought to investigate whether T-type Ca2+ channels (TCCs) in the brain are involved in generating post-anesthetic hyperexcitatory behaviors (PAHBs). We found that younger rat pups (postnatal days 9–11) had a higher incidence of PAHBs and higher PAHB scores than older pups (postnatal days 16–18) during emergence from sevoflurane anesthesia. The power spectrum of the theta oscillations (4 Hz–8 Hz) in the prefrontal cortex was significantly enhanced in younger pups when PAHBs occurred, while there were no significant changes in older pups. Both the power of theta oscillations and the level of PAHBs were significantly reduced by the administration of TCC inhibitors. Moreover, the sensitivity of TCCs in the medial dorsal thalamic nucleus to sevoflurane was found to increase with age by investigating the kinetic properties of TCCs in vitro. TCCs were activated by potentiated GABAergic depolarization with a sub-anesthetic dose of sevoflurane (1%). These data suggest that (1) TCCs in the brain contribute to the generation of PAHBs and the concomitant electroencephalographic changes; (2) the stronger inhibitory effect of sevoflurane contributes to the lack of PAHBs in older rats; and (3) the contribution of TCCs to PAHBs is not mediated by a direct effect of sevoflurane on TCCs. [ABSTRACT FROM AUTHOR]

The primary task of RFID readers is to identify multiple objects as quickly and reliably as possible with minimal power consumption and computation. One issue addressed in this study is the manner in which RFID systems adopting the framed slotted ALOHA (FSA) protocol should be configured so that the minimum identification delay can be achieved with a preassigned confidence level. We first formalize two optimization problems associated with the configuration of passive RFID tag identification processes, i.e., the determination of two key parameter values-termination time and frame size. Next, by considering a comparative RFID system, which well approximates the original system, we establish closed-form formulas for the aforementioned parameters. Furthermore, through asymptotic analysis, we investigate the asymptotic behavior of the optimal parameter values as functions of number of tags and confidence level. Through numerical comparisons, we verify the effectiveness of the proposed solutions. [ABSTRACT FROM AUTHOR]

Thin-film solid-oxide fuel cells (TF-SOFCs) fabricated using microelectromechanical systems (MEMS) processing techniques not only help lower the cell operating temperature but also provide a convenient platform for studying cathodic losses. Utilizing these platforms, cathode kinetics can be enhanced dramatically by engineering the microstructure of the cathode/electrolyte interface by increasing the surface grain-boundary density. Nanoscale secondary ion mass spectrometry and high-resolution transmission electron microscopy studies have shown that oxygen exchange at electrolyte surface grain boundaries is facilitated by a high population of oxide-ion vacancies segregating preferentially to the grain boundaries. Furthermore, three-dimensional structuring of TF-SOFCs enabled by various lithography methods also helps increase the active surface area and enhance the surface exchange reaction. Although their practical prospects are yet to be verified, MEMS-based TF-SOFC platforms hold the potential to provide high-performance for low-temperature SOFC applications. [ABSTRACT FROM AUTHOR]