Kadah Yasser M
Biomedical Engineering Department, Cairo University, Giza, Egypt.
IEEE Trans Biomed Eng. 2004 Nov;51(11):1944-53. doi: 10.1109/TBME.2004.831525.
A new adaptive signal-preserving technique for noise suppression in event-related functional magnetic resonance imaging (fMRI) data is proposed based on spectral subtraction. The proposed technique estimates a parametric model for the power spectrum of random noise from the acquired data based on the characteristics of the Rician statistical model. This model is subsequently used to estimate a noise-suppressed power spectrum for any given pixel time course by simple subtraction of power spectra. The new technique is tested using computer simulations and real data from event-related fMRI experiments. The results show the potential of the new technique in suppressing noise while preserving the other deterministic components in the signal. Moreover, we demonstrate that further analysis using principal component analysis and independent component analysis shows a significant improvement in both convergence and clarity of results when the new technique is used. Given its simple form, the new method does not change the statistical characteristics of the signal or cause correlated noise to be present in the processed signal. This suggests the value of the new technique as a useful preprocessing step for fMRI data analysis.
基于谱减法，提出了一种用于事件相关功能磁共振成像（fMRI）数据噪声抑制的新型自适应信号保留技术。所提出的技术基于莱斯统计模型的特征，从采集的数据中估计随机噪声功率谱的参数模型。随后，通过简单地减去功率谱，该模型用于估计任何给定像素时间历程的噪声抑制功率谱。使用计算机模拟和来自事件相关fMRI实验的真实数据对新技术进行了测试。结果表明，新技术在抑制噪声的同时保留信号中其他确定性成分具有潜力。此外，我们证明，当使用新技术时，使用主成分分析和独立成分分析进行进一步分析在结果的收敛性和清晰度方面都有显著改善。鉴于其简单的形式，新方法不会改变信号的统计特征，也不会在处理后的信号中产生相关噪声。这表明新技术作为fMRI数据分析有用的预处理步骤的价值。
