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Exposure to ionizing radiation disrupts metabolic pathways and causes oxidative stress, which can lead to organ damage. In this study, urinary metabolites from mice exposed to high-dose and low-dose whole-body irradiation (WBI HDR, WBI LDR) or partial-body irradiation (PBI BM2.5) were analyzed using targeted and untargeted metabolomics approaches.

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While radiation hazards induced by cone-beam computed tomography (CBCT) in image-guided radiotherapy (IGRT) can be reduced by sparse-view sampling, the image quality is inevitably degraded. We propose a deep learning-based multi-view projection synthesis (DLMPS) approach to improve the quality of sparse-view low-dose CBCT images. In the proposed DLMPS approach, linear interpolation was first applied to sparse-view projections and the projections were rearranged into sinograms; these sinograms were processed with a sinogram restoration model and then rearranged back into projections.

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Background: The effects of ionizing radiation (IR) involve a highly orchestrated series of events in cells, including DNA damage and repair, cell death, and changes in the level of proliferation associated with the stage of the cell cycle. A large number of existing studies in literature have examined the activity of genes and their regulators in mammalian cells in response to high doses of ionizing radiation. Although there are many studies, the research in effect of low doses of ionizing radiation remains limited.

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Purpose: Conventional radiotherapy (CRT) has limited local control and poses a high risk of severe toxicity in large lung tumors. This study aimed to develop an integrated treatment plan that combines CRT with lattice boost radiotherapy (LRT) and monitors its dosimetric characteristics.

Methods: This study employed cone-beam computed tomography from 115 lung cancer patients to develop a U-Net +  + deep learning model for generating synthetic CT (sCT).

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Noise reduction of low-dose electron holograms using the wavelet hidden Markov model.

Microscopy (Oxf)

January 2025

The Ultramicroscopy Research Center, Kyushu University, 744 Motooka, Fukuoka 819-0395, Japan.

The precision in electron holography studies on electrostatic and magnetic fields depends on the image quality of an electron hologram. Enhancing the image quality of electron holograms is essential for the comprehensive analysis of weak electromagnetic fields; however, extended electron beam irradiation can lead to undesirable radiation damage and contamination. Recent studies have demonstrated that noise reduction using the wavelet hidden Markov model (WHMM) can improve the precision of phase analysis for limited thin-foiled crystals.

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