NDLI: 31P-MRS data analysis of liver based on self-organizing map neural networks

Content Provider	IEEE Xplore Digital Library
Author	Qiang Liu Yan-hong Ma Ning Wang Yi-hui Liu Shao-qing Wang Li-juan Wang Jin-yong Cheng Jie Chen Dong-yue Yu
Copyright Year	2009
Description	Author affiliation: Taishan Medical College, Taian Shandong China, 271000 (Ning Wang; Jie Chen; Dong-yue Yu) \|\| School of Information Science and Technology, Shandong Institute of Light Industry, Jinan China, 250353 (Yi-hui Liu; Li-juan Wang; Jin-yong Cheng) \|\| MRI Department of Shandong Medical Imaging Research Institute, Jinan Shandong China, 250021 (Qiang Liu; Shao-qing Wang) \|\| Weifang Medical College, Shandong China, 261042 (Yan-hong Ma)
Abstract	Objective: Discussion based on neural networks in the $^{31}P$ MR spectroscopy to distinguish hepatocellular carcinoma, normal liver and cirrhosis in value. Methods: Using self-organizing map neural network (SOM) analyse 66 data of $^{31}P$ MRS, including hepatocellular carcinoma (13 samples), normal liver (16 samples) and liver cirrhosis (37 samples). Results: $^{31}P$ MRS can be used for the diagnosis and differential diagnosis between hepatocellular carcinoma and liver cirrhosis nodules. The four experiments show that neural network model based on the $^{31}P$ MR spectroscopy data analysis may increase diagnostic accuracy rate of hepatocellular carcinoma from 85.4% to 92.31%. Conclusion: $^{31}P$ MRS data analysis based on neural network model provides a valuable diagnostic means of of hepatocellular carcinoma in vivo.
Starting Page	151
Ending Page	153
File Size	1138710
Page Count	3
File Format	PDF
ISBN	9781424446063
DOI	10.1109/PACIIA.2009.5406618
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Spectroscopy Data analysis Liver magnetic resonance spectroscopy neural network Biological neural networks 31-Phosphorus In vivo Magnetic resonance imaging Neural networks Biopsy Medical diagnostic imaging hepatocellular carcinoma Biomedical imaging
Content Type	Text
Resource Type	Article

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