Abstract: OEF can be quantitatively calculated using fMRI images based on BOLD effect and a two-component model, and it may be helpful in clinical prognosing and diagnosing of cerebrovascular disease. However, the Signal-Noise-Ratio（SNR） of the image is so poor that it’s urgent to research and design efficient denoising algorithm in order to improve accuracy of OEF computing. In this paper, a wavelet denoising algorithm based on Bayesian estimating the adaptive threshold is designed and applied to analyse and denoise fMRI images based on BOLD effect, and result images are provided to the post processing and computing OEF. Experimental result shows that the algorithm in this paper can improve accuracy of OEF computing effectively.

Key words: oxygen extraction fraction, image denoise, wavelet analysis, Bayesian estimation

摘要： 利用基于BOLD（Blood Oxygenation Level Dependent）效应的fMRI图像和两室模型可以定量计算脑氧摄取分数（Oxygen Extraction Fraction，OEF），在脑血管病的预测和诊断上有较大的临床应用价值。但由于BOLD效应fMRI图像的信噪比较低，研究并设计有效的BOLD效应fMRI图像去噪算法，从而提高OEF计算结果的准确性是急需解决的问题。因此，设计了基于贝叶斯估计的自适应阈值小波去噪方法对BOLD效应fMRI图像进行分析和去噪，并将结果图像应用于OEF值的计算。实验结果表明该方法能有效提高OEF计算结果的准确性。

关键词: 脑氧摄取分数, 图像去噪, 小波分析, 贝叶斯估计