The global pandemic of coronavirus disease 2019 (COVID-19) has resulted in an increased demand for testing, diagnosis, and treatment. Reverse transcription polymerase chain reaction (RT-PCR) is the definitive test for the diagnosis of COVID-19; however, chest X-ray radiography (CXR) is a fast, effective, and affordable test that identifies the possible COVID-19-related pneumonia. This study investigates the feasibility of using a deep learning-based decision-tree classifier for detecting COVID-19 from CXR images. The proposed classifier comprises three binary decision trees, each trained by a deep learning model with convolution neural network based on the PyTorch frame. The first decision tree classifies the CXR images as normal or abnormal. The second tree identifies the abnormal images that contain signs of tuberculosis, whereas the third does the same for COVID-19. The accuracies of the first and second decision trees are 98 and 80%, respectively, whereas the average accuracy of the third decision tree is 95%. The proposed deep learning-based decision-tree classifier may be used in pre-screening patients to conduct triage and fast-track decision making before RT-PCR results are available.

A Cerenkov fiber-optic dosimeter (CFOD) is fabricated using plastic optical fibers to measure Cerenkov radiation induced by a therapeutic photon beam. We measured the Cerenkov radiation generated in optical fibers in various irradiation conditions to evaluate the usability of Cerenkov radiation for a photon beam therapy dosimetry. As a results, the spectral peak of Cerenkov radiation was measured at a wavelength of 515 nm, and the intensity of Cerenkov radiation increased linearly with increasing irradiated length of the optical fiber. Also, the intensity peak of Cerenkov radiation was measured in the irradiation angle range of 30 to 40 deg. In the results of Monte Carlo N-particle transport code simulations, the relationship between fluxes of electrons over Cerenkov threshold energy and energy deposition of a 6 MV photon beam had a nearly linear trend. Finally, percentage depth doses for the 6 MV photon beam could be obtained using the CFOD and the results were compared with those of an ionization chamber. Here, the mean dose difference was about 0.6%. It is anticipated that the novel and simple CFOD can be effectively used for measuring depth doses in radiotherapy dosimetry.

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The purpose of this study was to improve the performance of a small gamma camera, utilizing a NaI(Tl) plate and a 5" position sensitive PMT. We attempted to build a NaI(Tl) plate crystal system which retained all its advantages, while at the same time integrating some of the advantages inherent in an array-type scintillation crystal system. Flood images were obtained with a lead hole mask, and position mapping was performed by detecting hole positions in the flood image. Energy calibration was performed using the energy spectra obtained from each hole position. Flood correction was performed using a uniformity correction table containing the relative efficiency of each image element. The spatial resolution was improved about 16% after correction at the centre field of view. Resolution deterioration at the outer field of view (OFOV) was considerably ameliorated, from 6.7 mm to 3.2 mm after correction. The sensitivity at the OFOV was also increased after correction, from 0.7 cps microCi(-1) to 2.0 cps microCi(-1). The correction also improved uniformity, from 5.2% to 2.1%, and linearity, from 0.5 mm to 0 mm. The results of this study indicate that the revised correction method can be employed to considerably improve the performance of a small gamma camera using a NaI(Tl) plate-type crystal. This method also provides high spatial resolution and linearity, like array-type crystals do, while retaining the specific advantages of plate-type crystals.

Abstract-The analysis in this paper concerns the performance of smart antenna algorithms when used in code-division multiple access (CDMA) wireless communication systems. Complex pseudonoise (PN) spreading, despreading, and pilot-aided channel estimates in the cdma2000 reverse link are some of major characteristics that are different from those in the IS-95 CDMA systems. These different features are included in our analysis. Four computationally efficient smart antenna algorithms are introduced: 1) smart antenna based on maximum output power criteria without lagrange multiplier; 2) smart antenna based on maximum signal-to-interference-plus-noise output power ratio (SINR) criteria with eigenvector solution; 3) smart antenna based on maximum SINR output criteria without eigenvector solution; 4) more simplified smart antenna based on maximum SINR output criteria without eigenvector solution. Algorithms (1) and (4) require only 4 computational instruction cycles per snapshot where is the number of antenna array elements. Algorithms (2) and (3) require 2 and (4 + 2 2 ) operations per snapshot, respectively. These computational loads are significantly smaller than those of typical eigenvalue decomposition blind detection approaches. Bit error rates (BERs) resulting from these algorithms are evaluated through simulation. Double spike power delay profile with equal or unequal power is used. Also, a cluster of interfering users and scattered interference users are considered. For BER comparisons, antenna diversity using equal gain combining is also analyzed. The four smart antenna algorithms show significant capacity improvement compared to the antenna array diversity using equal gain combining under the double spike power delay profile with equal power and scattered interference environments.Index Terms-Adaptive array antenna, code division multiple access (CDMA), mobile fading environment, multiple access interference, smart antenna, wireless communications.

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This study develops an improved Feldkamp–Davis–Kress (FDK) reconstruction algorithm using non-local total variation (NLTV) denoising and a cubic B-spline interpolation-based backprojector to enhance the image quality of low-dose cone-beam computed tomography (CBCT). The NLTV objective function is minimized on all log-transformed projections using steepest gradient descent optimization with an adaptive control of the step size to augment the difference between a real structure and noise. The proposed algorithm was evaluated using a phantom data set acquired from a low-dose protocol with lower milliampere-seconds (mAs).The combination of NLTV minimization and cubic B-spline interpolation rendered the enhanced reconstruction images with significantly reduced noise compared to conventional FDK and local total variation with anisotropic penalty. The artifacts were remarkably suppressed in the reconstructed images. Quantitative analysis of reconstruction images using low-dose projections acquired from low mAs showed a contrast-to-noise ratio with spatial resolution comparable to images reconstructed using projections acquired from high mAs. The proposed approach produced the lowest RMSE and the highest correlation. These results indicate that the proposed algorithm enables application of the conventional FDK algorithm for low mAs image reconstruction in low-dose CBCT imaging, thereby eliminating the need for more computationally demanding algorithms. The substantial reductions in radiation exposure associated with the low mAs projection acquisition may facilitate wider practical applications of daily online CBCT imaging.