Making the Invisible Visible: Toward High-Quality Terahertz Tomographic Imaging via Physics-Guided Restoration.

Terahertz (THz) tomographic imaging has recently attracted significant attention thanks to its non-invasive, non-destructive, non-ionizing, material-classification, and ultra-fast nature for object exploration and inspection. However, its strong water absorption nature and low noise tolerance lead to undesired blurs and distortions of reconstructed THz images. The diffraction-limited THz signals highly constrain the performances of existing restoration methods. To address the problem, we propose a novel multi-view Subspace-Attention-guided Restoration Network (SARNet) that fuses multi-view and multi-spectral features of THz images for effective image restoration and 3D tomographic reconstruction. To this end, SARNet uses multi-scale branches to extract intra-view spatio-spectral amplitude and phase features and fuse them via shared subspace projection and self-attention guidance. We then perform inter-view fusion to further improve the restoration of individual views by leveraging the redundancies between neighboring views. Here, we experimentally construct a THz time-domain spectroscopy (THz-TDS) system covering a broad frequency range from 0.1 to 4 THz for building up a temporal/spectral/spatial/material THz database of hidden 3D objects. Complementary to a quantitative evaluation, we demonstrate the effectiveness of our SARNet model on 3D THz tomographic reconstruction applications. Supplementary Information: The online version contains supplementary material available at 10.1007/s11263-023-01812-y.

Terahertz spatio-temporal deep learning computed tomography.

Terahertz computed tomography (THz CT) has drawn significant attention because of its unique capability to bring multi-dimensional object information from invisible to visible. However, current physics-model-based THz CT modalities present low data use efficiency on time-resolved THz signals and low model fusion extensibility, limiting their application fields' practical use. In this paper, we propose a supervised THz deep learning computed tomography (THz DL-CT) framework based on time-domain information. THz DL-CT restores superior THz tomographic images of 3D objects by extracting features from spatio-temporal THz signals without any prior material information. Compared with conventional and machine learning based methods, THz DL-CT delivers at least 50.2%, and 52.6% superior in root mean square error (RMSE) and structural similarity index (SSIM), respectively. Additionally, we have experimentally demonstrated that the pretrained THz DL-CT model can generalize to reconstruct multi-material systems with no prerequisite information. THz CT through the DL data fusion approach provides a new pathway for non-invasive functional imaging in object investigation.

Systematic Evaluation of Antioxidant Efficiency and Antibacterial Mechanism of Bitter Gourd Extract Stabilized Silver Nanoparticles.

In this study, we accentuate the facile and green synthesis of ecologically viable silver nanoparticles (AgNPs) using aqueous (A-BGE) and ethanolic (E-BGE) dried bitter gourd (Momordica charantia) fruit extract as reducing and capping agents. Although AgNPs synthesized using BGEs have been reported earlier in fundamental antimicrobial studies, the possible antioxidant activity, antibacterial efficacy against superbugs, and a potential antimicrobial mechanism are still lacking. The characterization of as-prepared AgNPs was studied through UV-vis, TEM, Zeta-potential, FT-IR, XRD, and XPS analysis. The antioxidant ability of BG-AgNPs was extensively evaluated through DPPH and FRAP assays, which showed that A-BG-AgNPs possessed higher scavenging ability and superior reducing power due to the high phenolic content present in the BG extract. Furthermore, A-BG-AgNPs were highly stable in various physiological media and displayed excellent antibacterial activity against drug-resistant bacterial strains (i.e., MIC value of 4 µg/mL). The generation of reactive oxygen species evidenced that the possible antimicrobial mechanism was induced by BG-AgNPs, resulting in bacterial cell damage. Within the minimal hemolysis, the BG-mediated AgNPs possessed synergistic antioxidant and antibacterial agents and open another avenue for the inhibition of the growth of pathogens.