Ultra-high field functional magnetic resonance imaging (fMRI) has allowed us to acquire images with submillimetre voxels. However, in order to interpret the data clearly, we need to accurately correct head motion and the resultant distortions. Here, we present a novel application of Boundary Based Registration (BBR) to realign functional Magnetic Resonance Imaging (fMRI) data and evaluate its effectiveness on a set of 7T submillimetre data, as well as millimetre 3T data for comparison. BBR utilizes the boundary information from high contrast present in structural data to drive registration of functional data to the structural data. In our application, we realign each functional volume individually to the structural data, effectively realigning them to each other. In addition, this realignment method removes the need for a secondary aligning of functional data to structural data for purposes such as laminar segmentation or registration to data from other scanners. We demonstrate that BBR realignment outperforms standard realignment methods across a variety of data analysis methods. For instance, the method results in a 15% increase in linear discriminant contrast, a cross-validated estimate of multivariate discriminability. Further analysis shows that this benefit is an inherent property of the BBR cost function and not due to the difference in target volume. Our results show that BBR realignment is able to accurately correct head motion in 7T data and can be utilized in preprocessing pipelines to improve the quality of 7T data.

中文翻译：

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使用基于边界的配准 (BBR) 改进亚毫米 7T fMRI 时间序列的运动校正

超高场功能磁共振成像 (fMRI) 使我们能够获取亚毫米体素的图像。然而，为了清楚地解释数据，我们需要准确地校正头部运动和由此产生的失真。在这里，我们提出了基于边界配准 (BBR) 的新应用，以重新调整功能磁共振成像 (fMRI) 数据，并评估其对一组 7T 亚毫米数据以及毫米 3T 数据的有效性以进行比较。BBR 利用结构数据中存在的高对比度的边界信息来驱动功能数据与结构数据的配准。在我们的应用程序中，我们将每个功能体积单独重新对齐到结构数据，有效地将它们相互重新对齐。此外，这种重新对齐方法消除了将功能数据与结构数据进行二次对齐的需要，例如层流分割或其他扫描仪数据的配准。我们证明了 BBR 重新对齐在各种数据分析方法中优于标准重新对齐方法。例如，该方法导致线性判别对比度增加 15%，这是多变量判别性的交叉验证估计。进一步的分析表明，这种好处是 BBR 成本函数的固有属性，而不是由于目标量的差异。我们的结果表明，BBR 重新对齐能够准确地校正 7T 数据中的头部运动，并可用于预处理管道以提高 7T 数据的质量。