Pseudo low-energy monochromatic imaging of head and neck cancers: Deep learning image reconstruction with dual-energy CT.

PURPOSE: Low-energy virtual monochromatic images (VMIs) derived from dual-energy computed tomography (DECT) systems improve lesion conspicuity of head and neck cancer over single-energy CT (SECT). However, DECT systems are installed in a limited number of facilities; thus, only a few facilities benefit from VMIs. In this work, we present a deep learning (DL) architecture suitable for generating pseudo low-energy VMIs of head and neck cancers for facilities that employ SECT imaging. METHODS: We retrospectively analyzed 115 patients with head and neck cancers who underwent contrast enhanced DECT. VMIs at 70 and 50 keV were used as the input and ground truth (GT), respectively. We divided them into two datasets: for DL (104 patients) and for inference with SECT (11 patients). We compared four DL architectures: U-Net, DenseNet-based, and two ResNet-based models. Pseudo VMIs at 50 keV (pVMI50keV) were compared with the GT in terms of the mean absolute error (MAE) of Hounsfield unit (HU) values, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). The HU values for tumors, vessels, parotid glands, muscle, fat, and bone were evaluated. pVMI50keV were generated from actual SECT images and the HU values were evaluated. RESULTS: U-Net produced the lowest MAE (13.32 ± 2.20 HU) and highest PSNR (47.03 ± 2.33 dB) and SSIM (0.9965 ± 0.0009), with statistically significant differences (P < 0.001). The HU evaluation showed good agreement between the GT and U-Net. U-Net produced the smallest absolute HU difference for the tumor, at < 5.0 HU. CONCLUSION: Quantitative comparisons of physical parameters demonstrated that the proposed U-Net could generate high accuracy pVMI50keV in a shorter time compared with the established DL architectures. Although further evaluation on diagnostic accuracy is required, our method can help obtain low-energy VMI from SECT images without DECT systems.

Exposure to Radiofrequency Electromagnetic Field in the High-Frequency Band and Cognitive Function in Children and Adolescents: A Literature Review.

With increasing use of mobile phones, exposure to radiofrequency electromagnetic field (RF-EMF) in the high-frequency band associated with mobile phones has become a public concern, with potentially adverse effects on cognitive function in children and adolescents. However, findings regarding the relation of RF-EMF and cognitive function in children and adolescents have been inconsistent due to a number of study design-related factors, such as types of exposure and outcome measures, age of participants, and the era of study conduction. The present literature review focused on these possible factors that could explain this inconsistency. This review identified 12 eligible studies (participants ages 4 to 17 years) and extracted a total 477 relations. In total, 86% of the extracted relations were not statistically significant; in the remaining 14%, a negative relation between RF-EMF and cognitive performance was detected under limited conditions: when (1) RF-EMF was assessed using objective measurement not subjective measurement (i.e., questionnaire), (2) participants were relatively older (12 years and above) and had greater opportunity of exposure to RF-EMF, and (3) the collection of cognitive function data was conducted after 2012. Given that 86% of the extracted relations in this analysis were not statistically significant, the interpretation should be approached with caution due to the possibility of the 14% of significant relationships, extracted in this review, representing chance findings.