Functional MRI (fMRI) is widely used to study the functional organization of normal and pathological brains. However, the fMRI signal may be contaminated by subject motion artifacts that are only partially mitigated by motion correction strategies. These artifacts lead to distance-dependent biases in the inferred signal correlations. To mitigate these spurious effects, motion-corrupted volumes are censored from fMRI time series. Censoring can result in discontinuities in the fMRI signal, which may lead to substantial alterations in functional connectivity analysis. We propose a new approach to recover the missing entries from censoring based on structured low rank matrix completion. We formulated the artifact-reduction problem as the recovery of a super-resolved matrix from unprocessed fMRI measurements. We enforced a low rank prior on a large structured matrix, formed from the samples of the time series, to recover the missing entries. The recovered time series, in addition to being motion compensated, are also slice-time corrected at a fine temporal resolution. To achieve a fast and memory-efficient solution for our proposed optimization problem, we employed a variable splitting strategy. We validated the algorithm with simulations, data acquired under different motion conditions, and datasets from the ABCD study. Functional connectivity analysis showed that the proposed reconstruction resulted in connectivity matrices with lower errors in pair-wise correlation than non-censored and censored time series based on a standard processing pipeline. In addition, seed-based correlation analyses showed improved delineation of the default mode network. These demonstrate that the method can effectively reduce the adverse effects of motion in fMRI analysis.

中文翻译：

---

减少功能磁共振成像中运动伪影的影响：结构化矩阵完成方法


功能性磁共振成像 (fMRI) 广泛用于研究正常和病理大脑的功能组织。然而，fMRI 信号可能会受到对象运动伪影的污染，而运动校正策略只能部分减轻这些伪影。这些伪影导致推断的信号相关性中存在距离相关的偏差。为了减轻这些虚假影响，从功能磁共振成像时间序列中删除了运动损坏的体积。审查可能会导致功能磁共振成像信号的不连续性，这可能会导致功能连接分析的实质性改变。我们提出了一种基于结构化低秩矩阵补全的新方法来恢复审查中丢失的条目。我们将伪影减少问题表述为从未处理的功能磁共振成像测量中恢复超分辨矩阵。我们对由时间序列样本形成的大型结构化矩阵强制执行低秩先验，以恢复丢失的条目。恢复的时间序列除了进行运动补偿之外，还以精细的时间分辨率进行切片时间校正。为了为我们提出的优化问题实现快速且节省内存的解决方案，我们采用了变量分割策略。我们通过模拟、不同运动条件下采集的数据以及 ABCD 研究的数据集验证了该算法。功能连接分析表明，与基于标准处理流程的非审查和审查时间序列相比，所提出的重建所产生的连接矩阵在成对相关性中的误差更低。此外，基于种子的相关性分析显示默认模式网络的描绘得到了改善。这些表明该方法可以有效减少fMRI分析中运动的不利影响。