PND-Net: Physics based Non-local Dual-domain Network for Metal Artifact Reduction

Metal artifacts caused by the presence of metallic implants tremendously degrade the reconstructed computed tomography (CT) image quality, affecting clinical diagnosis or reducing the accuracy of organ delineation and dose calculation in radiotherapy. Recently, deep learning methods in sinogram and image domains have been rapidly applied on metal artifact reduction (MAR) task. The supervised dual-domain methods perform well on synthesized data, while unsupervised methods with unpaired data are more generalized on clinical data. However, most existing methods intend to restore the corrupted sinogram within metal trace, which essentially remove beam hardening artifacts but ignore other components of metal artifacts, such as scatter, non-linear partial volume effect and noise. In this paper, we mathematically derive a physical property of metal artifacts which is verified via Monte Carlo (MC) simulation and propose a novel physics based non-local dual-domain network (PND-Net) for MAR in CT imaging. Specifically, we design a novel non-local sinogram decomposition network (NSD-Net) to acquire the weighted artifact component, and an image restoration network (IR-Net) is proposed to reduce the residual and secondary artifacts in the image domain. To facilitate the generalization and robustness of our method on clinical CT images, we employ a trainable fusion network (F-Net) in the artifact synthesis path to achieve unpaired learning. Furthermore, we design an internal consistency loss to ensure the integrity of anatomical structures in the image domain, and introduce the linear interpolation sinogram as prior knowledge to guide sinogram decomposition. Extensive experiments on simulation and clinical data demonstrate that our method outperforms the state-of-the-art MAR methods.Comment: 19 pages, 8 figure

Thiophene Disubstituted Benzothiadiazole Derivatives: An Effective Planarization Strategy Toward Deep-Red to Near-Infrared (NIR) Organic Light-Emitting Diodes

As one of the three primary colors that are indispensable in full-color displays, the development of red emitters is far behind the blue and green ones. Here, three novel orange-yellow to near-infrared (NIR) emitters based on 5,6-difluorobenzo[c][1,2,5]thiadiazole (BTDF) namely BTDF-TPA, BTDF-TTPA, and BTDF-TtTPA were designed and synthesized. Density functional theory analysis and photophysical characterization reveal that these three materials possess hybridized local and charge-transfer (HLCT) state feature and a feasible reverse intersystem crossing (RISC) from the high-lying triplet state to the singlet state may conduce to an exciton utilization exceeding the limit of 25% of traditional fluorescence materials under electrical excitation. The insertion of thiophene with small steric hindrance as π-bridge between the electron-donating (D) moiety triphenylamine (TPA) and the electron-accepting (A) moiety BTDF not only results in a remarkable 67 nm red-shift of the emission peak but also brings about a large overlap of frontier molecular orbitals to guarantee high radiative transition rate that is of great significance to obtain high photoluminescence quantum yield (PLQY) in the “energy-gap law” dominated long-wavelength emission region. Consequently, an attractive high maximum external quantum efficiency (EQE) of 5.75% was achieved for the doped devices based on these thiophene π-bridged emitters, giving a deep-red emission with small efficiency roll-off. Remarkably, NIR emission could be obtained for the non-doped devices, achieving an excellent maximum EQE of 1.44% and Commission Internationale de l'Éclairage (CIE) coordinates of (0.71, 0.29). These results are among the highest efficiencies in the reported deep-red to NIR fluorescent OLEDs and offer a new π-bridge design strategy in D-π-A and D-π-A-π-D red emitter design