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 functio...

实时功能磁共振成像（rtfMRI）是一种近年兴起的技术，要求在单个重复时间（TR，repetition time）内完成快速数据处理，例如TR为2秒。rtfMRI的数据预处理很少涉及空间标准化（spatial normalization），因为该步骤无法在短时间内完成。然而，空间标准化对于立体定向空间中准确的功能定位至关重要，也是rtfMRI一些新兴应用的必要步骤。本研究提出了一种在线空间标准化方法，该方法采用基于主轴配准（principal axes registration，PA）和自适应β参数的高斯-牛顿优化（Gauss-Newton optimization，GN）的新型仿射配准（affine registration，AFR）步骤（称为PA-GN(β) AFR），以及基于离散余弦变换（discrete cosine transform，DCT）的非线性配准（nonlinear registration，NLR）。在AFR中，PA为GN提供了合适的初始估计，以促进GN的快速收敛。此外，β参数依赖于代价函数的变化率...