Data from: Online spatial normalization for real-time fMRI

Real-time functional magnetic resonance imaging (rtfMRI) is a recently emerged technique that demands fast data processing within a single repetition time (TR), such as a TR of 2 seconds. Data preprocessing in rtfMRI has rarely involved spatial normalization, which can not be accomplished in a short time period. However, spatial normalization may be critical for accurate functional localization in a stereotactic space and is an essential procedure for some emerging applications of rtfMRI. In this study, we introduced an online spatial normalization method that adopts a novel affine registration (AFR) procedure based on principal axes registration (PA) and Gauss-Newton optimization (GN) using the self-adaptive β parameter, termed PA-GN(β) AFR and nonlinear registration (NLR) based on discrete cosine transform (DCT). In AFR, PA provides an appropriate initial estimate of GN to induce the rapid convergence of GN. In addition, the β parameter, which relies on the change rate of cost function, is employed to self-adaptively adjust the iteration step of GN. The accuracy and performance of PA-GN(β) AFR were confirmed using both simulation and real data and compared with the traditional AFR. The appropriate cutoff frequency of the DCT basis function in NLR was determined to balance the accuracy and calculation load of the online spatial normalization. Finally, the validity of the online spatial normalization method was further demonstrated by brain activation in the rtfMRI data.

实时功能磁共振成像（real-time functional magnetic resonance imaging，rtfMRI）是近年来新兴的技术，要求在单个重复时间（repetition time，TR）内完成快速数据处理，例如2秒的TR。rtfMRI的数据预处理极少涉及空间标准化，因为该操作无法在短时间内完成。然而，空间标准化对于立体定位空间中的精准功能定位至关重要，同时也是rtfMRI部分新兴应用的必要流程。本研究提出了一种在线空间标准化方法，该方法包含两类配准流程：其一为基于主轴配准（principal axes registration，PA）与高斯-牛顿优化（Gauss-Newton optimization，GN）的新型仿射配准（affine registration，AFR），并通过自适应β参数对其进行优化，将该仿射配准方法命名为PA-GN(β) AFR；其二为基于离散余弦变换（discrete cosine transform，DCT）的非线性配准（nonlinear registration，NLR）。在仿射配准流程中，主轴配准可为高斯-牛顿优化提供合适的初始估计值，从而促使其快速收敛。此外，本方法还通过基于代价函数变化率的β参数，自适应调整高斯-牛顿优化的迭代步长。研究通过仿真数据与真实数据验证了PA-GN(β) AFR的准确性与性能，并将其与传统仿射配准方法进行了对比。研究还确定了非线性配准中离散余弦变换基函数的最优截止频率，以平衡在线空间标准化的准确性与计算负载。最后，通过rtfMRI数据中的脑激活结果，进一步验证了该在线空间标准化方法的有效性。