Publications by authors named "Yuri Teraoka"
Pseudo low-energy monochromatic imaging of head and neck cancers: Deep learning image reconstruction with dual-energy CT.
Int J Comput Assist Radiol Surg
July 2022
Article Synopsis
- Low-energy virtual monochromatic images (VMIs) from dual-energy CT (DECT) enhance the visibility of head and neck cancer lesions compared to single-energy CT (SECT), but DECT availability is limited.
- A study analyzed the effectiveness of a deep learning (DL) model, specifically U-Net, in generating pseudo low-energy VMIs from SECT images by evaluating data from 115 patients with head and neck cancers.
- U-Net outperformed other DL architectures, yielding the best accuracy in mimicking actual VMIs, making it a promising alternative for facilities without DECT systems, although further research is needed to confirm its diagnostic value.
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.
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