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© 2021 Institute of Physics and Engineering in Medicine.High cone-angle artifacts (HCAAs) appear frequently in circular cone-beam computed tomography (CBCT) images and can heavily affect diagnosis and treatment planning. To reduce HCAAs in CBCT scans, we propose a novel deep learning approach that reduces the three-dimensional (3D) nature of HCAAs to two-dimensional (2D) problems in an efficient way. Specifically, we exploit the relationship between HCAAs and the rotational scanning geometry by training a convolutional neural network (CNN) using image slices that were radially sampled from CBCT scans. We evaluated this novel approach using a dataset of input CBCT scans affected by HCAAs and high-quality artifact-free target CBCT scans. Two different CNN architectures were employed, namely U-Net and a mixed-scale dense CNN (MS-D Net). The artifact reduction performance of the proposed approach was compared to that of a Cartesian slice-based artifact reduction deep learning approach in which a CNN was trained to remove the HCAAs from Cartesian slices. In addition, all processed CBCT scans were segmented to investigate the impact of HCAAs reduction on the quality of CBCT image segmentation. We demonstrate that the proposed deep learning approach with geometry-aware dimension reduction greatly reduces HCAAs in CBCT scans and outperforms the Cartesian slice-based deep learning approach. Moreover, the proposed artifact reduction approach markedly improves the accuracy of the subsequent segmentation task compared to the Cartesian slice-based workflow.

Acute rhinosinusitis is a commonly seen disease that is frequently diagnosed based on clinical symptomatology without any objective evidence. The most common form of treatment is antibiotics, even though many cases may not have a bacterial etiology. The most common bacterial organisms in childhood sinusitis belong to the group that cause many childhood upper respiratory and lower respiratory infections and include Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis, and Staphylococcus aureus. Besides antibiotics, other treatments are aimed at assisting with drainage, and this can be accomplished by reducing the inflammation and edema that prevents venting of the sinus structures. These medications can include antihistamines, decongestants, intranasal steroids, nasal saline irrigation, and, in some countries, mucolytics. Complementary and alternative medicine (CAM) techniques including Chinese herbal medications are often used, but there is little scientific evidence to support their use. The use of immunotherapy has not been found to be effective in the management of acute rhinosinusitis in children or adults.

Much research has been done in the field of epigenetics to evaluate potential roles as both biomarkers for different diseases and as therapeutic agents. Epigenetic transformation is described as changes that are both reversible and hereditable. These modifications include DNA methylation and histone modifications through acetylation, deacetylation, and histone deacetylation enzyme inhibitors. MicroRNAs have also been suspected of playing a role in affecting gene expression, yet whether or not these oligonucleotides truly represent an epigenetic phenomenon is still a topic of much debate. While these alterations have long been used as targets in cancer therapies for decades, they have shown recent promise in tracking and possibly treating other diseases such as autoimmune diseases, neurologic diseases, dermatologic processes, asthma, and allergies through alterations in gene expression. While epigenetics is being increasingly thought of as the most exciting, cutting edge frontier in medicine, one should proceed with caution and humility. All treatments or drugs have side effects, and epigenetic therapy is no different. The danger is when the side effects are not immediately discernable, and the nature of epigenetics is such that we may not appreciate the side effects of manipulating the genetic machinery for generations to come.