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首页> 外文期刊>Journal of Neuroscience Methods >A Mathematical Model for Understanding the STatistical effects of k-space (AMMUST-k) preprocessing on observed voxel measurements in fcMRI and fMRI.
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A Mathematical Model for Understanding the STatistical effects of k-space (AMMUST-k) preprocessing on observed voxel measurements in fcMRI and fMRI.

机译：用于了解k空间（AMMUST-k）预处理对fcMRI和fMRI中观察到的体素测量的静态影响的数学模型。

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摘要

Image processing is common in functional magnetic resonance imaging (fMRI) and functional connectivity magnetic resonance imaging (fcMRI). Such processing may have deleterious effects on statistical maps computed from the processed images. In this manuscript, we describe a mathematical framework to evaluate the effects of image processing on observed voxel means, covariances and correlations resulting from linear processes on k-space and image-space data. We develop linear operators for common image processing operations, including: zero-filling, apodization, smoothing and partial Fourier reconstruction; and unmodeled physical processes, including: Fourier encoding anomalies caused by eddy currents, intra-acquisition decay and magnetic field inhomogeneities. With such operators, we theoretically compute the exact image-space means, covariances and correlations which result from their common implementation and verify their behavior in experimental phantom data. Thus, a very powerful framework is described to consider the effects of image processing on observed voxel means, covariances and correlations. With this framework, researchers can theoretically consider observed voxel correlations while understanding the extent of artifactual correlations resulting from image processing. Furthermore, this framework may be utilized in the future to theoretically optimize image acquisition parameters, and examine the order of image processing steps.
机译：图像处理在功能磁共振成像（fMRI）和功能连接磁共振成像（fcMRI）中很常见。这样的处理可能对从处理后的图像计算出的统计图具有有害影响。在此手稿中，我们描述了一个数学框架，用于评估图像处理对所观察到的体素均值，协方差和因对k空间和图像空间数据进行线性处理而产生的相关性的影响。我们为常见的图像处理操作开发线性算子，包括：零填充，切趾，平滑和部分傅里叶重构;以及未建模的物理过程，包括：由涡流，采集内衰减和磁场不均匀性引起的傅立叶编码异常。使用这样的算子，我们从理论上计算出由它们的共同实现产生的确切的图像空间均值，协方差和相关性，并验证了它们在实验体模数据中的行为。因此，描述了一个非常强大的框架来考虑图像处理对观察到的体素均值，协方差和相关性的影响。有了这个框架，研究人员可以在理论上考虑观察到的体素相关性，同时了解图像处理导致的人为相关性的程度。此外，将来可以使用此框架来从理论上优化图像采集参数，并检查图像处理步骤的顺序。

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