Purpose Dual‐energy computed tomography (DECT) is highly promising for material characterization and identification, whereas reconstructed material‐specific images are affected by magnified noise and beam‐hardening artifacts. Although various DECT material decomposition methods have been proposed to solve this problem, the quality of the decomposed images is still unsatisfactory, particularly in the image edges. In this study, a data‐driven approach using dual interactive Wasserstein generative adversarial networks (DIWGAN) is developed to improve DECT decomposition accuracy and perform edge‐preserving images. Methods In proposed DIWGAN, two interactive generators are used to synthesize decomposed images of two basis materials by modeling the spatial and spectral correlations from input DECT reconstructed images, and the corresponding discriminators are employed to distinguish the difference between the generated images and labels. The DECT images reconstructed from high‐ and low‐energy bins are sent to two generators separately, and each generator synthesizes one material‐specific image, thereby ensuring the specificity of the network modeling. In addition, the information from different energy bins is exploited through the feature sharing of two generators. During decomposition model training, a hybrid loss function including L1 loss, edge loss, and adversarial loss is incorporated to preserve the texture and edges in the generated images. Additionally, a selector is employed to define the generator that should be trained in each iteration, which can ensure the modeling ability of two different generators and improve the material decomposition accuracy. The performance of the proposed method is evaluated using digital phantom, XCAT phantom, and real data from a mouse. Results On the digital phantom, the regions of bone and soft tissue are strictly and accurately separated using the trained decomposition model. The material densities in different bone and soft‐tissue regions are near the ground truth, and the error of material densities is lower than 3 mg/ml. The results from XCAT phantom show that the material‐specific images generated by directed matrix inversion and iterative decomposition methods have severe noise and artifacts. Regarding to the learning‐based methods, the decomposed images of fully convolutional network (FCN) and butterfly network (Butterfly‐Net) still contain varying degrees of artifacts, while proposed DIWGAN can yield high quality images. Compared to Butterfly‐Net, the root‐mean‐square error (RMSE) of soft‐tissue images generated by the DIWGAN decreased by 0.01 g/ml, whereas the peak‐signal‐to‐noise ratio (PSNR) and structural similarity (SSIM) of the soft‐tissue images reached 31.43 dB and 0.9987, respectively. The mass densities of the decomposed materials are nearest to the ground truth when using the DIWGAN method. The noise standard deviation of the decomposition images reduced by 69%, 60%, 33%, and 21% compared with direct matrix inversion, iterative decomposition, FCN, and Butterf...

During the past two decades, most researchers employed a questionnaire to characterize the effect of noise on psychosomatic responses. Developments in physiological techniques offer a non-invasive method for recording brain activity with electroencephalography (EEG). This method for assessing the impact of noise on attention is growing in popularity. The aim of this study was to investigate brain activity changes in response to noise exposure during attention-demanding tasks by using EEG power and phase coherence estimation. We hypothesized that brain rhythms could be affected by environmental stimuli and would be reflected in the EEG power and phase coherence. Nineteen healthy right-handed university students (mean age = 21.5 ± 2.0 years) participated in this study. The experiment comprised recording EEG data for participants in the following steps: rest with eyes closed (< 50 dBA), rest with eyes open, listening in a noisy environment (85 dBA), performance on an attention-demanding task in a quiet environment (< 50 dBA), and performance on an attention-demanding task in a noisy environment (85 dBA). Significant differences were observed between stages, and the participants performed more effectively in the quiet environment, where they showed higher rates of correct responses (p <.05). From the assessment of the EEG power and phase coherence estimation, the study demonstrated the following: (1) Alpha-2 (10-13 Hz) power and phase coherence decreased when participants shifted from closed eyes to open eyes, while theta power increased. (2) In contrast, during the noise exposure phase, whether during an attention-demanding task or not, beta (13-30 Hz) phase coherence decreased in the brain, but theta phase coherence was not affected compared to the results in the quiet environment. We suggest that the high frequency of neural synchronization is relevant for cognitive performance, and that participants at risk for selective attention are affected by noise exposure.