Proposing an intelligent technique based on radial basis function neural network to forecast the energy spectrum of diagnostic X-ray imaging systems

doi:10.1016/j.apradiso.2023.110961

科研成果详情

题名	Proposing an intelligent technique based on radial basis function neural network to forecast the energy spectrum of diagnostic X-ray imaging systems
作者	Zhanjian, Cai 1; Zheng, Jiadi 2; Shan, Liu 3; Wei, Wang 4; Zhu, Wenzong 5; Lu, Yanjie 6; Zhang, Xicai 7; Guoqiang, Xu 8
发表日期	2023-10
发表期刊	APPLIED RADIATION AND ISOTOPES 影响因子和分区
语种	英语
原始文献类型	Article ; Journal Article
关键词	Energy spectra Diagnostic Radial basis function neural network
其他关键词	MAMMOGRAPHY ; PERFORMANCE ; RADIOLOGY ; ARTIFACTS ; MODELS ; CT
摘要	In digital subtraction angiography (digital subtraction total cerebral angiography), cardiac and macrovascular cardiography, anorectal radiology, fluoroscopy, and computed tomography (CT), a prior knowledge to X-ray energy spectrum is crucial for assessing the image quality and also calculating patient X-ray dosage. The present investigation's main objective is to propose an intelligent technique for faster calculating X-ray energy spectrum of medical imaging systems with different exposure settings of tube voltage, filter material, and thickness based on limited specific spectra. In this study, Monte Carlo N Particle (MCNP) simulation code was initially used to generate some limited X-ray spectra for tube voltages of 20, 30, 40, 50, 80, 100, 130, and 150 kV for two different filters of beryllium and aluminum with thicknesses of 0. 4, 0.8, 1.2, 1.6 and 2 mm. Tube voltage, type, and thickness of filter were used as the 3 inputs of 150 Radial Basis Function Neural Network (RBFNN) to forecast point by point of the X-ray spectrum. After training, the RBFNNs could forecast most of the X-ray spectra for tube voltages in the range of 20-150 kV and two various filters of aluminum and beryllium with thicknesses in the range of 0-2 mm.
出版者	PERGAMON-ELSEVIER SCIENCE LTD
ISSN	0969-8043
EISSN	1872-9800
卷号	200
DOI	10.1016/j.apradiso.2023.110961
页数	8
WOS类目	Chemistry, Inorganic & Nuclear ; Nuclear Science & Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向	Chemistry ; Nuclear Science & Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001148955800001
收录类别	SCIE ; PUBMED ; EI ; SCOPUS
在线发表日期	2023-07
EI入藏号	20233214489463
EI主题词	Aluminum
EI分类号	461.6 Medicine and Pharmacology ; 541.1 Aluminum ; 542.1 Beryllium and Alloys ; 549 Nonferrous Metals and Alloys ; 723.5 Computer Applications ; 746 Imaging Techniques ; 922.2 Mathematical Statistics
URL	查看原文
PubMed ID	3753173
SCOPUSEID	2-s2.0-85166488875
通讯作者地址	[Zhang, Xicai]Pingyang Hospital Affiliated to Wenzhou Medical University,Wenzhou,China ; [Guoqiang, Xu]Yongkang First People's Hospital,Department of Neurology,China
Scopus学科分类	Radiation
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/209446
专题	药学院（分析测试中心）其他_附属平阳医院（平阳县人民医院）
通讯作者	Zhang, Xicai; Guoqiang, Xu
作者单位	1.Anorectal Department,The Third People's Hospital of Hangzaou,Hangzhou,311115,China; 2.Zhejiang Chinese Medical University,China; 3.Wenzhou Medical University,China; 4.School of Pharmacy,Wenzhou Medical University,Wenzhou,325000,China; 5.Zhejiang Chinese Medical University,Hangzhou,310000,China; 6.Department of Digital Media Technology,Hangzhou Dianzi University,Hangzhou,310018,China; 7.Pingyang Hospital Affiliated to Wenzhou Medical University,Wenzhou,China; 8.Yongkang First People's Hospital,Department of Neurology,China
通讯作者单位	附属平阳医院（平阳县人民医院）
推荐引用方式 GB/T 7714	Zhanjian, Cai,Zheng, Jiadi,Shan, Liu,et al. Proposing an intelligent technique based on radial basis function neural network to forecast the energy spectrum of diagnostic X-ray imaging systems[J]. APPLIED RADIATION AND ISOTOPES,2023,200.
APA	Zhanjian, Cai., Zheng, Jiadi., Shan, Liu., Wei, Wang., Zhu, Wenzong., ... & Guoqiang, Xu. (2023). Proposing an intelligent technique based on radial basis function neural network to forecast the energy spectrum of diagnostic X-ray imaging systems. APPLIED RADIATION AND ISOTOPES, 200.
MLA	Zhanjian, Cai,et al."Proposing an intelligent technique based on radial basis function neural network to forecast the energy spectrum of diagnostic X-ray imaging systems".APPLIED RADIATION AND ISOTOPES 200(2023).