| 王韶卿,刘毅慧,王丽娟,刘强,成金勇,李保朋.基于反向传输神经网络的肝脏31P磁共振波谱分析[J].中国医学影像技术,2009,25(10):1875~1878 |
| 基于反向传输神经网络的肝脏31P磁共振波谱分析 |
| 31P-MRS data analysis of liver based on back-propagation neural networks |
| 投稿时间：2009-03-27 修订日期：2009-06-01 |
| DOI： |
| 中文关键词: 磷-31 磁共振波谱 肝肿瘤 神经网络 |
| 英文关键词:31P-hosphorus Magnetic resonance spectroscopy Liver neoplasms Neural network |
| 基金项目:山东省自然科学基金(Y2006C96)。 |
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| 摘要点击次数: 2084 |
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| 中文摘要: |
| 目的 探讨基于神经网络的31P磁共振波谱(31P-MRS)辨别肝硬化、肝细胞癌(HCC)和正常肝组织的价值。方法 运用反向传输神经网络分析66个31P-MRS样本数据,其中包括37个肝硬化结节样本、13个HCC样本和16个正常肝脏样本。结果 经交叉验证实验证明,基于神经网络模型的31P-MR波谱数据分析可以将肝细胞癌的诊断正确率从85.47%提高到92.31%。结论 基于神经网络模型的31P-MRS波谱数据分析可以用于HCC与肝硬化结节的诊断和鉴别诊断。 |
| 英文摘要: |
| Objective To explore the value of distinguishment of hepatocellular carcinoma (HCC), cirrhosis nodules and normal liver based on neural networks in the 31P-MR spectroscopy. Methods A total of 66 data of 31P-MRS were analysed using back-propagation neural network, including 37 samples of liver cirrhosis, 13 samples of HCC and 16 samples of normal liver. Results The cross-valiation experiments showed that diagnostic accuracy rate of HCC increased from 85.47% to 92.31% with neural network model based on the 31P-MR spectroscopy data analysis. Conclusion 31P-MRS data analysis based on neural network model provides a valuable diagnostic tool of HCC in vivo. |
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