Jeong Seongah, Li Xiang, Yang Jiarui, Li Quanzheng, Tarokh Vahid
School of Electronics Engineering, Kyungpook National University, Daegu 14566, South Korea.
Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
IEEE Access. 2020;8:36728-36740. doi: 10.1109/access.2020.2971261. Epub 2020 Feb 3.
In the field of neuroimaging and cognitive neuroscience, functional Magnetic Resonance Imaging (fMRI) has been widely used to study the functional localization and connectivity of the brain. However, the inherently low signal-to-noise ratio (SNR) of the fMRI signals greatly limits the accuracy and resolution of current studies. In addressing this fundamental challenge in fMRI analytics, in this work we develop and implement a denoising method for task fMRI (tfMRI) data in order to delineate the high-resolution spatial pattern of the brain activation and functional connectivity via dictionary learning and sparse coding (DLSC). In addition to the traditional unsupervised dictionary learning model which has shown success in image denoising, we further utilize the prior knowledge of task paradigm to learn a dictionary consisting of both data-driven and model-driven terms for a more stable sparse representation of the data. The proposed method is applied to preprocess the motor tfMRI dataset from Human Connectome Project (HCP) for the purpose of brain activation detection and functional connectivity estimation. Comparison between the results from original and denoised fMRI data shows that the disruptive brain activation and functional connectivity patterns can be recovered, and the prominence of such patterns is improved through denoising. The proposed method is then compared with the temporal non-local means (tNLM)-based denoising method and shows consistently superior performance in various experimental settings. The promising results show that the proposed DLSC-based fMRI denoising method can effectively reduce the noise level of the fMRI signals and increase the interpretability of the inferred results, therefore constituting a crucial part of the preprocessing pipeline and provide the foundation for further high-resolution functional analysis.
在神经成像和认知神经科学领域，功能磁共振成像（fMRI）已被广泛用于研究大脑的功能定位和连通性。然而，fMRI信号固有的低信噪比（SNR）极大地限制了当前研究的准确性和分辨率。为了解决fMRI分析中的这一基本挑战，在这项工作中，我们开发并实现了一种用于任务fMRI（tfMRI）数据的去噪方法，以便通过字典学习和稀疏编码（DLSC）描绘大脑激活和功能连通性的高分辨率空间模式。除了在图像去噪方面已取得成功的传统无监督字典学习模型外，我们还进一步利用任务范式的先验知识来学习一个由数据驱动和模型驱动项组成的字典，以便对数据进行更稳定的稀疏表示。所提出的方法被应用于预处理来自人类连接组计划（HCP）的运动tfMRI数据集，以进行大脑激活检测和功能连通性估计。原始fMRI数据和去噪后fMRI数据的结果比较表明，破坏性的大脑激活和功能连通性模式可以被恢复，并且通过去噪提高了这些模式的显著性。然后将所提出的方法与基于时域非局部均值（tNLM）的去噪方法进行比较，结果表明在各种实验设置下，该方法始终具有优越的性能。这些有前景的结果表明，所提出的基于DLSC的fMRI去噪方法可以有效降低fMRI信号的噪声水平，提高推断结果的可解释性，因此构成了预处理流程的关键部分，并为进一步的高分辨率功能分析提供了基础。
