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

Safety and immunogenicity of an Escherichia coli-produced bivalent human papillomavirus type 6/11 vaccine: A dose-escalation, randomized, double-blind, placebo-controlled phase 1 trial

A dose-escalation, randomized, double-blind, placebo-controlled phase 1 clinical trial enrolled 145 eligible participants aged 18–55 years in March 2015 in Liuzhou, China. Stratified by age and sex, the participants were randomly assigned to receive either 30, 60, or 90 μg of the HPV-6/11 vaccine (n = 41/40/40) or the parallel placebo vaccine (n = 8/8/8) with a 0/1/6-month dose-escalation schedule. Participants were actively followed-up to record local and systemic AEs occurring within 30 days after each vaccination, and SAEs occurred in 7 months. Blood and urine samples of each participant were collected before and 2 days after the first and third vaccination to determine changes in routine blood, serum biochemical, and urine indexes. Serum HPV-6/11-specific IgG and neutralizing antibody levels at month 7 were analyzed. A total of 79 adverse events were reported, and no SAEs occurred. The incidences of total adverse reactions in the 30 μg, 60 μg, and 90 μg HPV vaccine groups and the control group were 31.7%, 50.0%, 42.5%, and 62.5%, respectively. All but one of the adverse reactions was mild or moderate with grade 1 or 2. No vaccine-related changes with clinical significance were found in paired blood and urine indexes before and after vaccinations. All the participants in the per-protocol set seroconverted at month 7 for both IgG and neutralizing antibodies. The candidate novel Escherichia-coli-produced bivalent HPV-6/11 vaccine has been preliminarily proven to be well tolerated and with robust immunogenicity in a phase 1 clinical study, supporting further trials with larger sample size. The study has been registered at ClinicalTrials.gov (NCT02405520