Background: Item memory and source memory are differently processed with both behavioral and event-related potential (ERP) evidence. Reality monitoring, a specific type of source memory, which refers to the ability to differentiate external sources from internal sources, has been drawing much attention. Among factors that have an impact on reality monitoring, fluency has not been well-studied. Therefore, the current study aimed to investigate whether fluency could affect reality monitoring, through observations on both behavioral performance and electrophysiological patterns. Material/Methods: Adopting ERP techniques, participants were required either to watch the presentation of a name/picture pair, or to imagine a picture for each displayed name, once (low fluency) or twice (high fluency). Later they completed a reality monitoring task of identifying names as perceived, imagined, or novel items. Behavioral performance was measured, and ERP waveforms were recorded. Results: Behaviorally, high fluency items were faster and more accurately attributed to the sources than low fluency items. ERP waveforms revealed that late positive component (LPC) occurred for all 4 types of items, while imagined items of low fluency did not record a robust FN400 or late frontal old/new effect. Conclusions: As results revealed, the factor of fluency does influence reality monitoring in terms of accuracy and responding speed. Meanwhile, for imagined items of low fluency, the absence of FN400 and frontal old/new effect also suggests the sensitivity of reality monitoring to fluency, because these representatives of familiarity-based processing and post-retrieval monitoring are inevitably involved in the process of differentiating internal source from external source.
Objective: Computed tomography (CT) and magnetic resonance imaging (MRI) are widely used in medical imaging modalities, and provide valuable information for clinical diagnosis and treatment. However, due to hardware limitations and radiation safety concerns, the acquired images are often limited in resolution. Super-resolution reconstruction (SR) techniques have been developed to enhance resolution of CT and MRI slices, which can potentially improve diagnostic accuracy. To capture more useful feature information and reconstruct higher quality super-resolution images, we proposed a novel hybrid framework SR model based on generative adversarial networks (GANs). 
Approach: The proposed SR model combines frequency domain and perceptual loss functions, which can work in both frequency domain and image domain. The proposed method consists of 4 parts: i) the discrete Fourier transform (DFT) operation transforms the image from the image domain to frequency domain; ii) a complex residual U-net performs SR in the frequency domain; iii) the inverse discrete Fourier transform (iDFT) operation based on data fusion transforms the image from the frequency domain to image domain; iv) an enhanced residual U-net network is used for SR of image domain. 
Main results: Experimental results on bladder MRI slices, abdomen CT slices, and brain MRI slices show that the proposed SR model outperforms state-of-the-art SR methods in terms of visual quality and objective quality metric such as the structural similarity (SSIM) and the peak signal-to-noise ratio (PSNR), which proves that the proposed model has better generalization and robustness. (Bladder dataset: upscaling factor of 2: SSIM=0.913, PSNR=31.203; upscaling factor of 4: SSIM=0.821, PSNR=28.604. Abdomen dataset: upscaling factor of 2: SSIM=0.929, PSNR=32.594; upscaling factor of 4: SSIM=0.834, PSNR=27.050. Brain dataset: SSIM=0.861, PSNR=26.945).
Significance: Our proposed SR model is capable of super-resolution reconstruction for CT and MRI slices. The SR results provide a reliable and effective foundation for clinical diagnosis and treatment.
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