Application of a Novel Nd:YAG/PPMgLN Laser Module Speckle-Suppressed by Multi-Mode Fibers in an Exhibition Environment

Laser exhibition technology has been widely used in the virtual environment of exhibitions and shows, as well as in the physical conference and exhibition centers. However, the speckle issue due to the high coherence of laser sources has caused harmful impacts on image quality, which is one of the obstacles to exhibition effects. In this paper, we design a compact Nd:YAG/PPMgLN laser module at 561.5 nm and use two different types of big-core multi-mode fibers to lower the spatial coherence. According to our experiment, the speckle contrasts relating to these two types reduce to 7.9% and 4.1%, respectively. The results of this paper contribute to improving the application effects of key optical components in the exhibitions. Only in this way can we provide technical supports and service guarantee for the development of the exhibition activities, and an immersive interactive experience for the audiences.

Comparison of Image Quality and Spatial Resolution Between 18F, 68Ga and 64Cu Phantom Measurements Using a Digital Biograph Vision PET/CT

Background: The PET nuclide and reconstruction method can have a considerable influence on spatial resolution and image quality of PET/CT scans, which can, for example, influence the diagnosis in oncology. The individual impact of the positron energy of 18F, 68Ga and 64Cu on spatial resolution and image quality of PET/CT scans acquired using a clinical, digital scanner was compared. Furthermore, the impact of different reconstruction parameters on image quality and spatial resolution was evaluated for 18F-FDG PET/CT scans acquired with a scanner of the newest generation. Methods: PET/CT scans of a Jaszczak phantom and a NEMA PET body phantom, filled with 18F-FDG, 68Ga-HCl and 64Cu-HCl, respectively, were performed on a Siemens Biograph Vision. Images were assessed using spatial resolution and image quality (Recovery Coefficients (RC), coefficient of variation within the background, Contrast Recovery Coefficient (CRC), Contrast-Noise-Ratio (CNR), and relative count error in lung insert). In a subsequent analysis, the scan of the NEMA PET body phantom filled with 18F-FDG was reconstructed applying different parameters (with/without the application of Point Spread Function (PSF), Time of Flight (ToF) or post-filtering; matrix size). Spatial resolution and quantitative image quality were compared between reconstructions. Results: We found that image quality was comparable between 18F-FDG and 64Cu-HCl PET/CT measurements featuring similar maximal endpoint energy. In comparison, RC, CRC and CNR were worse in 68Ga-HCl data, despite similar count rates. Spatial resolution was up to 18 % worse in 68Ga-HCl compared to 18F-FDG images. Post-filtering of 18F-FDG acquisitions changed image quality the most and reduced spatial resolution by 52 % if a Gaussian filter with 5 mm FWHM was applied. ToF measurements especially improved the recovery of the smallest lesion (RCmean = 1.07 compared to 0.65 without ToF) and improved spatial resolution by 29 %.Conclusions: The positron energy of PET nuclides influences spatial resolution and image quality of digital PET/CT scans. Image quality of 68Ga-HCl PET/CT images was worse compared to 18F-FDG and 64Cu-HCl, respectively, despite similar count rates. Reconstruction parameters have a high impact on image quality and spatial resolution and should be considered when comparing images of different scanners or centers.

Simulated Annealing-Based Image Reconstruction for Patients With COVID-19 as a Model for Ultralow-Dose Computed Tomography

The proposed algorithm of inverse problem of computed tomography (CT), using limited views, is based on stochastic techniques, namely simulated annealing (SA). The selection of an optimal cost function for SA-based image reconstruction is of prime importance. It can reduce annealing time, and also X-ray dose rate accompanying better image quality. In this paper, effectiveness of various cost functions, namely universal image quality index (UIQI), root-mean-squared error (RMSE), structural similarity index measure (SSIM), mean absolute error (MAE), relative squared error (RSE), relative absolute error (RAE), and root-mean-squared logarithmic error (RMSLE), has been critically analyzed and evaluated for ultralow-dose X-ray CT of patients with COVID-19. For sensitivity analysis of this ill-posed problem, the stochastically estimated images of lung phantom have been reconstructed. The cost function analysis in terms of computational and spatial complexity has been performed using image quality measures, namely peak signal-to-noise ratio (PSNR), Euclidean error (EuE), and weighted peak signal-to-noise ratio (WPSNR). It has been generalized for cost functions that RMSLE exhibits WPSNR of 64.33 ± 3.98 dB and 63.41 ± 2.88 dB for 8 × 8 and 16 × 16 lung phantoms, respectively, and it has been applied for actual CT-based image reconstruction of patients with COVID-19. We successfully reconstructed chest CT images of patients with COVID-19 using RMSLE with eighteen projections, a 10-fold reduction in radiation dose exposure. This approach will be suitable for accurate diagnosis of patients with COVID-19 having less immunity and sensitive to radiation dose.

Cross-Domain Feature Similarity Guided Blind Image Quality Assessment

This work proposes an end-to-end cross-domain feature similarity guided deep neural network for perceptual quality assessment. Our proposed blind image quality assessment approach is based on the observation that features similarity across different domains (e.g., Semantic Recognition and Quality Prediction) is well correlated with the subjective quality annotations. Such phenomenon is validated by thoroughly analyze the intrinsic interaction between an object recognition task and a quality prediction task in terms of characteristics of the human visual system. Based on the observation, we designed an explicable and self-contained cross-domain feature similarity guided BIQA framework. Experimental results on both authentical and synthetic image quality databases demonstrate the superiority of our approach, as compared to the state-of-the-art models.