Feiyu Chen

Compressed sensing (CS) reconstruction methods leverage sparse structure in underlying signals to recover high-resolution images from highly undersampled measurements. When applied to magnetic resonance imaging (MRI), CS has the potential to dramatically shorten MRI scan times, increase diagnostic value, and improve overall patient experience. However, CS has several shortcomings which limit its clinical translation such as: 1) artifacts arising from inaccurate sparse modelling assumptions, 2) extensive parameter tuning required for each clinical application, and 3) clinically infeasible reconstruction times. Recently, CS has been extended to incorporate deep neural networks as a way of learning complex image priors from historical exam data. Commonly referred to as unrolled neural networks, these techniques have proven to be a compelling and practical approach to address the challenges of sparse CS. In this tutorial, we will review the classical compressed sensing formulation and outline steps needed to transform this formulation into a deep learning-based reconstruction framework. Supplementary open source code in Python will be used to demonstrate this approach with open databases. Further, we will discuss considerations in applying unrolled neural networks in the clinical setting.

Purpose To develop a deep learning reconstruction approach to improve the reconstruction speed and quality of highly undersampled variable-density single-shot fast spin-echo imaging by using a variational network (VN), and to clinically evaluate the feasibility of this approach. Materials and Methods Imaging was performed with a 3.0-T imager with a coronal variable-density single-shot fast spin-echo sequence at 3.25 times acceleration in 157 patients referred for abdominal imaging (mean age, 11 years; range, 1-34 years; 72 males [mean age, 10 years; range, 1-26 years] and 85 females [mean age, 12 years; range, 1-34 years]) between March 2016 and April 2017. A VN was trained based on the parallel imaging and compressed sensing (PICS) reconstruction of 130 patients. The remaining 27 patients were used for evaluation. Image quality was evaluated in an independent blinded fashion by three radiologists in terms of overall image quality, perceived signal-to-noise ratio, image contrast, sharpness, and residual artifacts with scores ranging from 1 (nondiagnostic) to 5 (excellent). Wilcoxon tests were performed to test the hypothesis that there was no significant difference between VN and PICS. Results VN achieved improved perceived signal-to-noise ratio (P = .01) and improved sharpness (P < .001), with no difference in image contrast (P = .24) and residual artifacts (P = .07). In terms of overall image quality, VN performed better than did PICS (P = .02). Average reconstruction time ± standard deviation was 5.60 seconds ± 1.30 per section for PICS and 0.19 second ± 0.04 per section for VN. Conclusion Compared with the conventional parallel imaging and compressed sensing reconstruction (PICS), the variational network (VN) approach accelerates the reconstruction of variable-density single-shot fast spin-echo sequences and achieves improved overall image quality with higher perceived signal-to-noise ratio and sharpness. © RSNA, 2018 Online supplemental material is available for this article.

Immunotherapy sheds new light to cancer treatment and is satisfied by cancer patients. However, immunotoxicity, single-source antibodies, and single-targeting stratege are potential challenges to the success of cancer immunotherapy. A huge number of promising lead compounds for cancer treatment are of natural origin from herbal medicines. The application of natural products from herbal medicines that have immunomodulatory properties could alter the landscape of immunotherapy drastically. The present study summarizes current medication for cancer immunotherapy and discusses the potential chemicals from herbal medicines as immune checkpoint inhibitors that have a broad range of immunomodulatory effects. Therefore, this review provides valuable insights into the efficacy and mechanism of actions of cancer immunotherapies, including natural products and combined treatment with immune checkpoint inhibitors, which could confer an improved clinical outcome for cancer treatment.

Fast-charging metal-ion batteries are essential for advancing energy storage technologies, but their performance is often limited by the high activation energy (Ea) required for ion diffusion in solids. Addressing this challenge has been particularly difficult for multivalent ions like Zn2+. Here, we present an amorphous organic-hybrid vanadium oxide (AOH-VO), featuring one-dimensional chains arranged in a disordered structure with atomic/molecular-level pores for promoting hierarchical ion diffusion pathways and reducing Zn2+ interactions with the solid skeleton. AOH-VO cathode demonstrates an exceptionally low Ea of 7.8 kJ·mol−1 for Zn2+ diffusion in solids and 6.3 kJ·mol−1 across the cathode-electrolyte interface, both significantly lower than that of electrolyte (13.2 kJ·mol−1) in zinc ion battery. This enables ultrafast charge-discharge performance, with an Ah-level pouch cell achieving 81.3% of its capacity in just 9.5 minutes and retaining 90.7% capacity over 5000 cycles. These findings provide a promising pathway toward stable, ultrafast-charging battery technologies with near-barrier-free ion dynamics. Promoting solid ion-diffusion is essential for fast-charging battery. Here, authors present near-barrier-free ion dynamics in an amorphous organic-hybrid vanadium oxide-based zinc ion battery and developed Ah-level fast-charging pouch cell.

The dysregulation of hepatic lipid metabolism is one of the hallmarks in many liver diseases including alcoholic liver diseases (ALD) and non-alcoholic fatty liver diseases (NAFLD). Hepatic inflammation, lipoperoxidative stress as well as the imbalance between lipid availability and lipid disposal, are direct causes of liver steatosis. The application of herbal medicines with anti-oxidative stress and lipid-balancing properties has been extensively attempted as pharmaceutical intervention for liver disorders in experimental and clinical studies. Although the molecular mechanisms underlying their hepatoprotective effects warrant further exploration, increasing evidence demonstrated that many herbal medicines are involved in regulating lipid accumulation processes including hepatic lipolytic and lipogenic pathways, such as mitochondrial and peroxisomal β-oxidation, the secretion of very low density lipoprotein (VLDL), the non-esterified fatty acid (NEFA) uptake, and some vital hepatic lipogenic enzymes. Therefore, in this review, the pathways or crucial mediators participated in the dysregulation of hepatic lipid metabolism are systematically summarized, followed by the current evidences and advances in the positive impacts of herbal medicines and natural products on the lipid metabolism pathways are detailed. Furthermore, several herbal formulas, herbs or herbal derivatives, such as Erchen Dection, Danshen, resveratrol, and berberine, which have been extensively studied for their promising potential in mediating lipid metabolism, are particularly highlighted in this review.