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首页> 外文期刊>Measurement Science & Technology >Analyzing Rice distributed functional magnetic resonance imaging data: a Bayesian approach
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Analyzing Rice distributed functional magnetic resonance imaging data: a Bayesian approach

机译：分析莱斯分布式功能磁共振成像数据：贝叶斯方法

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摘要

Analyzing functional MRI data is often a hard task due to the fact that these periodic signals are strongly disturbed with noise. In many cases, the signals are buried under the noise and not visible, such that detection is quite impossible. However, it is well known that the amplitude measurements of such disturbed signals follow a Rice distribution which is characterized by two parameters. In this paper, an alternative Bayesian approach is proposed to tackle this two-parameter estimation problem. By incorporating prior knowledge into a mathematical framework, the drawbacks of the existing methods (i.e. the maximum likelihood approach and the method of moments) can be overcome. The performance of the proposed Bayesian estimator is analyzed theoretically and illustrated through simulations. Finally, the developed approach is successfully applied to measurement data for the analysis of functional MRI.
机译：由于这些周期性信号受到噪声的强烈干扰，因此分析功能性MRI数据通常是一项艰巨的任务。在许多情况下，信号被掩埋在噪声下并且不可见，因此很难进行检测。但是，众所周知，这种受干扰信号的幅度测量遵循赖斯分布，其特征在于两个参数。在本文中，提出了另一种贝叶斯方法来解决该两参数估计问题。通过将先验知识合并到数学框架中，可以克服现有方法（即最大似然法和矩量法）的缺点。理论上分析了提出的贝叶斯估计器的性能，并通过仿真进行了说明。最后，所开发的方法已成功应用于测量数据以进行功能性MRI分析。

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