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| 功能磁共振图像小波域分析结果的修正 | |
| 引用本文： | 支联合,单保慈,鲁娜,刘华,李琳,李可,闫镔. 功能磁共振图像小波域分析结果的修正[J]. 中国医学影像技术, 2006, 22(1): 32-34 |
| 作者姓名： | 支联合 单保慈 鲁娜 刘华 李琳 李可 闫镔 |
| 作者单位： | 1. 中国科学院高能物理研究所核分析技术重点实验室,北京,100049;中国科学院研究生院,北京,100049 2. 中国科学院高能物理研究所核分析技术重点实验室,北京,100049 3. 徐州医学院神经生物学研究中心,江苏,徐州,221002 |
| 基金项目： | 国家自然科学基金资助(90209030)。 |
| 摘 要： | 目的改进f MRI数据小波域分析方法。方法通过在原始空间域对传统小波方法检测出的激活区再进行检验来修正传统小波方法的缺点,并以SPM99为标准,通过比较传统小波方法和修正方法对一组手动实验数据的分析结果来说明修正方法的效果。结果修正方法能较好地除去或减小传统小波方法中激活区的扩散和伪影。结论小波域分析f MRI图像是一种快速灵敏的方法,但重建后激活区扩散且有伪影。本文提出的修正方法是一种快速且较传统小波方法准确的f MRI数据分析方法。 |
| 关 键 词： | 磁共振成像 功能性 小波变换 数据分析 |
| 文章编号： | 1003-3289（2006）01-0031-04 |
| 收稿时间： | 2005-08-10 |
| 修稿时间： | 2005-09-01 |
Modifying the analysis results of fMRI in wavelet domain |
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| ZHI Lian-he,SHAN Bao-ci,LU N,LIU Hu,LI Lin,LI Ke and YA N Bin. Modifying the analysis results of fMRI in wavelet domain[J]. Chinese Journal of Medical Imaging Technology, 2006, 22(1): 32-34 | |
| Authors: | ZHI Lian-he SHAN Bao-ci LU N LIU Hu LI Lin LI Ke YA N Bin |
| Affiliation: | 1. Key Laboratory of Nuclear Analysis Techniques, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; 2. Graduate School of the Chinese Academy of Sciences, Beijing 100049, China; 3. Department of Neurobiology, Xuzhou Medical College, Xuzhou 221002, China |
| Abstract: | Objective To improve the analysis method of fMRI data in wavelet domain. Methods The modifying method of wavelet domain was introduced by means of retesting activation areas of wavelet method by t-test in original spatial domain. The analysis results of the hand-motor experiment data by SPM 99, wavelet method and the modifying method were compared to demostrate the effect of the modifying method. Results The modifying method can effectively eliminate or decrease the spreading of activation areas and artifacts induced by wavelet method. Conclusion The analysis method of fMRI in wavelet domain is fast and sensitive, but its activation areas spread out after an inverse transform and have artifacts. The modifying method is fast and compared with wavelet method much accurate. |
| Keywords: | Magnetic resonance imaging functional Wavelet transform Data analysis |
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