Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands.
Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands.
Neuroimage. 2020 Jan 15;205:116127. doi: 10.1016/j.neuroimage.2019.116127. Epub 2019 Aug 30.
Nonlinearities of gradient magnetic fields in diffusion MRI (dMRI) can introduce systematic errors in estimates of diffusion measures. While there are correction methods that can compensate for these errors, as presented in the Human Connectome Project, such nonlinear effects are often assumed to be negligible for typical applications, and as a result, gradient nonlinearities are mostly left uncorrected. In this work, we perform a systematic analysis to investigate the effect of gradient nonlinearities on dMRI studies, from voxel-wise estimates to group study outcomes. We present a novel framework to quantify and visualize these effects by decomposing them into their magnitude and angle components. Mean magnitude deviation and fractional gradient anisotropy are introduced to quantify the distortions in the size and shape of gradient vector distributions. By means of Monte-Carlo simulations and real data from the Human Connectome Project, the errors on dMRI measures derived from the diffusion tensor imaging and diffusional kurtosis imaging are highlighted. We perform a group study to showcase the alteration in the significance and effect size due to ignoring gradient nonlinearity correction. Our results indicate that the effect of gradient field nonlinearities on dMRI studies can be significant and may complicate the interpretation of the results and conclusions.
扩散磁共振成像（dMRI）中梯度磁场的非线性会给扩散测量值的估计带来系统误差。虽然有一些校正方法可以补偿这些误差，如人类连接组计划中所提出的那样，但通常认为这些非线性效应对典型应用可以忽略不计，因此，梯度非线性大多未经校正。在这项工作中，我们进行了系统分析，以研究梯度非线性对 dMRI 研究的影响，从体素估计到组研究结果。我们提出了一种新的框架，通过分解它们的幅度和角度分量来量化和可视化这些效应。平均幅度偏差和分数各向异性用于量化梯度向量分布的大小和形状的扭曲。通过蒙特卡罗模拟和人类连接组计划的真实数据，突出了源自扩散张量成像和扩散峰度成像的 dMRI 测量值的误差。我们进行了一项组研究，展示了由于忽略梯度非线性校正而导致的显著性和效应量的变化。我们的结果表明，梯度场非线性对 dMRI 研究的影响可能是显著的，可能会使结果和结论的解释复杂化。
