Zobrazeno 1 - 10
of 115
pro vyhledávání: '"Weihai Zhuo"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-8 (2024)
Abstract The X-rays emitted during CT scans can increase solid cancer risks by damaging DNA, with the risk tied to patient-specific organ doses. This study aims to establish a new method to predict patient specific abdominal organ doses from CT exami
Externí odkaz:
https://doaj.org/article/763d545fbc2340e4b1a4f2e7f9b9708b
Publikováno v:
Zeitschrift für Medizinische Physik, Vol 34, Iss 3, Pp 408-418 (2024)
Purpose: The most common detector material in the PC CT system, cannot achieve the best performance at a relatively higher photon flux rate. In the reconstruction view, the most commonly used filtered back projection, is not able to provide sufficien
Externí odkaz:
https://doaj.org/article/354f2c61071245dd820b12f11480ffca
Publikováno v:
EJNMMI Physics, Vol 10, Iss 1, Pp 1-16 (2023)
Abstract Purpose Dynamic PET is an essential tool in oncology due to its ability to visualize and quantify radiotracer uptake, which has the potential to improve imaging quality. However, image noise caused by a low photon count in dynamic PET is mor
Externí odkaz:
https://doaj.org/article/3adaaa3cced64d61bad6b5b8370b10fb
Publikováno v:
Heliyon, Vol 9, Iss 10, Pp e20425- (2023)
Radon is the second leading risk factor for lung cancer after smoking. As a public policy, radon mitigation not only involves radon control technology or its cost-benefit analysis, but also includes the decision-making process of local governments. I
Externí odkaz:
https://doaj.org/article/a5ba14141c764cad8be7984a02806a73
Autor:
Ying Huang, Aihui Feng, Yang Lin, Hengle Gu, Hua Chen, Hao Wang, Yan Shao, Yanhua Duan, Weihai Zhuo, Zhiyong Xu
Publikováno v:
Radiation Oncology, Vol 17, Iss 1, Pp 1-9 (2022)
Abstract Background This study was designed to establish radiation pneumonitis (RP) prediction models using dosiomics and/or deep learning-based radiomics (DLR) features based on 3D dose distribution. Methods A total of 140 patients with non-small ce
Externí odkaz:
https://doaj.org/article/803fc6141a344d69a62ec6a23f8e259c
Publikováno v:
Radiation Medicine and Protection, Vol 2, Iss 4, Pp 155-159 (2021)
Iodine-131 is a highly toxic and volatile artificial radionuclide that is easily inhaled or ingested by the human body and selectively accumulates in thyroid tissue. With the development of nuclear medicine and nuclear power plants, the unintended re
Externí odkaz:
https://doaj.org/article/bb35971f4b5c42ee83831b4c5d0fe69d
Autor:
Bin Feng, Georg Steinhauser, Weihai Zhuo, Zhiling Li, Yupeng Yao, Tobias Blenke, Chao Zhao, Franz Renz, Bo Chen
Publikováno v:
Environment International, Vol 169, Iss , Pp 107505- (2022)
Anthropogenic release of tritium from nuclear facilities is expected to increase significantly in the coming decades, which may cause radiation exposure to humans through the contamination of water and food chains. It is necessary and urgent to acqui
Externí odkaz:
https://doaj.org/article/0bfbb6da19ac4329a305f56a64b7d5ad
Autor:
Pengcheng Hu, Xin Lin, Weihai Zhuo, Hui Tan, Tianwu Xie, Guobing Liu, Shuguang Chen, Xin Chen, Haojun Yu, Yiqiu Zhang, Hongcheng Shi, Haikuan Liu
Publikováno v:
EJNMMI Physics, Vol 8, Iss 1, Pp 1-17 (2021)
Abstract Purpose A 2-m axial field-of-view, total-body PET/CT scanner (uEXPLORER) has been recently developed to provide total-body coverage and ultra-high sensitivity, which together, enables opportunities for in vivo time-activity curve (TAC) measu
Externí odkaz:
https://doaj.org/article/544bec1338ed49de8a52b3089be4e284
Publikováno v:
Atmosphere, Vol 14, Iss 5, p 831 (2023)
Accurate measurement of low-level thoron gas and high-accuracy calibration of thoron measurement devices are essential for assessing and preventing thoron radiological risks. This study aimed to develop a thoron gas measurement technique using an air
Externí odkaz:
https://doaj.org/article/06f88227c2f44ffdaf0be86e640f80a3
Autor:
Ying Huang MS, Yifei Pi PhD, Kui Ma, Xiaojuan Miao, Sichao Fu ME, Zhen Zhu, Yifan Cheng, Zhepei Zhang, Hua Chen MS, Hao Wang MS, Hengle Gu MS, Yan Shao MS, Yanhua Duan ME, Aihui Feng MS, Weihai Zhuo PhD, Zhiyong Xu PhD
Publikováno v:
Technology in Cancer Research & Treatment, Vol 21 (2022)
Objectives: In this study, we propose a deep learning-based approach to predict Intensity-modulated radiation therapy (IMRT) quality assurance (QA) gamma passing rates using delivery fluence informed by log files. Methods: A total of 112 IMRT plans f
Externí odkaz:
https://doaj.org/article/a2a4b3d8714c4b67ba300b444c8ac7dc