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Limited-angle CT is a challenging problem in real applications. Incomplete projection data will lead to severe artifacts and distortions in reconstruction images. To tackle this problem, we propose a novel reconstruction framework termed Deep Iterative Optimization-based Residual-learning (DIOR) for limited-angle CT. Instead of directly deploying the regularization term on image space, the DIOR combines iterative optimization and deep learning based on the residual domain, significantly improving the convergence property and generalization ability. Specifically, the asymmetric convolutional modules are adopted to strengthen the feature extraction capacity in smooth regions for deep priors. Besides, in our DIOR method, the information contained in low-frequency and high-frequency components is also evaluated by perceptual loss to improve the performance in tissue preservation. Both simulated and clinical datasets are performed to validate the performance of DIOR. Compared with existing competitive algorithms, quantitative and qualitative results show that the proposed method brings a promising improvement in artifact removal, detail restoration and edge preservation. [ABSTRACT FROM AUTHOR]

X-ray computed tomography (CT) is of great clinical significance in medical practice because it can provide anatomical information about the human body without invasion, while its radiation risk has continued to attract public concerns. Reducing the radiation dose may induce noise and artifacts to the reconstructed images, which will interfere with the judgments of radiologists. Previous studies have confirmed that deep learning (DL) is promising for improving low-dose CT imaging. However, almost all the DL-based methods suffer from subtle structure degeneration and blurring effect after aggressive denoising, which has become the general challenging issue. This paper develops the Comprehensive Learning Enabled Adversarial Reconstruction (CLEAR) method to tackle the above problems. CLEAR achieves subtle structure enhanced low-dose CT imaging through a progressive improvement strategy. First, the generator established on the comprehensive domain can extract more features than the one built on degraded CT images and directly map raw projections to high-quality CT images, which is significantly different from the routine GAN practice. Second, a multi-level loss is assigned to the generator to push all the network components to be updated towards high-quality reconstruction, preserving the consistency between generated images and gold-standard images. Finally, following the WGAN-GP modality, CLEAR can migrate the real statistical properties to the generated images to alleviate over-smoothing. Qualitative and quantitative analyses have demonstrated the competitive performance of CLEAR in terms of noise suppression, structural fidelity and visual perception improvement. [ABSTRACT FROM AUTHOR]

The magnetic and magnetocaloric properties in GdNiBC compound have been investigated by magnetization and heat capacity measurements. The compound exhibits a paramagnetic to ferromagnetic transition at TC\sim 15 K. A large reversible magnetocaloric effect has been observed and accompanied by a second-order magnetic phase transition at TC . The maximum magnetic entropy change ( -\Delta SM^{\mathrm {max}} ) and adiabatic temperature change ( \Delta T\mathrm {ad}^{\mathrm {max}} ) are 24.9 J/kg K and 13 K, for a magnetic field change of 0–7 T, respectively. The corresponding relative cooling power is 801 J/kg. [ABSTRACT FROM AUTHOR]

The crystal structure, magnetism, and magnetocaloric effect (MCE) were investigated in ternary Er2Ni2Ga compound. The Er2Ni2Ga belongs to W2B2Co-type structure (orthorhombic, space group Immm). The magnetization measurements indicate that Er2Ni2Ga suffers a field-induced metamagnetic transition (antiferromagnetic–ferromagnetic) below Néel temperature (TN) of 7.1 K. Inverse MCE occurs with magnetic field change ($\Delta H$) lower than 0–30 kOe and at low temperatures, whereas normal reversible MCE happens with higher $\Delta \text{H}$. The calculated $-\Delta S_{M}$ (magnetic entropy change) has a peak value of 12.93 J/kg K (~9 K) with refrigerant capacity value of 265 J/kg (5–30 K) for Er2Ni2Ga under $\Delta H$ of 0–70 kOe. [ABSTRACT FROM AUTHOR]