Although functional magnetic resonance imaging (fMRI) data are acquired as complex-valued images, traditionally most fMRI studies only use the magnitude of the data. FMRI analysis in the complex domain promises to provide more statistically significant information; however, the noisy nature of the phase poses a challenge for successful study of fMRI by complex-valued signal processing algorithms. In this paper, we introduce a physiologically motivated de-noising method that uses phase quality maps to successfully identify and eliminate noisy areas in the fMRI data so they can be used in individual and group studies. Additionally, we show how the developed de-noising method improves the results of complex-valued independent component analysis of fMRI data, a very successful tool for blind source separation of biomedical data.

中文翻译：

---

用于复值 fMRI 数据去噪的质量图阈值及其在 fMRI ICA 中的应用。

尽管功能磁共振成像 (fMRI) 数据是作为复值图像获取的，但传统上大多数 fMRI 研究仅使用数据的幅度。复杂域中的 FMRI 分析有望提供更多具有统计意义的信息；然而，相位的噪声性质对通过复值​​信号处理算法成功研究 fMRI 提出了挑战。在本文中，我们介绍了一种生理动机去噪方法，该方法使用相位质量图成功识别和消除 fMRI 数据中的噪声区域，以便它们可用于个人和团体研究。此外，我们展示了开发的去噪方法如何改进 fMRI 数据的复值独立分量分析的结果，这是一种非常成功的生物医学数据盲源分离工具。