PDF(Pubmed)
Abstract：
Diffusion tensor imaging (DTI) metrics and tractography can be biased due to low signal-to-noise ratio (SNR) and systematic errors resulting from image artifacts and imperfections in magnetic field gradients. The imperfections include non-uniformity and nonlinearity, effects caused by eddy currents, and the influence of background and imaging gradients. We investigated the impact of systematic errors on DTI metrics of an isotropic phantom and DTI metrics and tractography of a rat brain measured at high resolution. We tested denoising and Gibbs ringing removal methods combined with the B matrix spatial distribution (BSD) method for magnetic field gradient calibration. The results showed that the performance of the BSD method depends on whether Gibbs ringing is removed and the effectiveness of stochastic error removal. Region of interest (ROI)-based analysis of the DTI metrics showed that, depending on the size of the ROI and its location in space, correction methods can remove systematic bias to varying degrees. The preprocessing pipeline proposed and dedicated to this type of data together with the BSD method resulted in an even - 90% decrease in fractional anisotropy (FA) (globally and locally) in the isotropic phantom and - 45% in the rat brain. The largest global changes in the rat brain tractogram compared to the standard method without preprocessing (sDTI) were noticed after denoising. The direction of the first eigenvector obtained from DTI after denoising, Gibbs ringing removal and BSD differed by an average of 56 and 10 degrees in the ROI from sDTI and from sDTI after denoising and Gibbs ringing removal, respectively. The latter can be identified with the amount of improvement in tractography due to the elimination of systematic errors related to imperfect magnetic field gradients. Based on the results, the systematic bias for high resolution data mainly depended on SNR, but the influence of non-uniform gradients could also be seen. After denoising, the BSD method was able to further correct both the metrics and tractography of the diffusion tensor in the rat brain by taking into account the actual distribution of magnetic field gradients independent of the examined object and uniquely dependent on the scanner and sequence. This means that in vivo studies are also subject to this type of errors, which should be taken into account when processing such data.
摘要：
由于低信噪比(SNR)和由磁场梯度中的图像伪影和缺陷导致的系统误差,扩散张量成像(DTI)度量和纤维束成像可能被偏置。缺陷包括非均匀性和非线性，涡流引起的影响，以及背景和成像梯度的影响。我们研究了系统误差对各向同性体模的DTI指标和高分辨率测量的DTI指标以及大鼠大脑的纤维束成像的影响。我们测试了去噪和吉布斯振铃去除方法与B矩阵空间分布（BSD）方法相结合的磁场梯度校准。结果表明，BSD方法的性能取决于吉布斯振铃是否被消除以及随机误差消除的有效性。基于感兴趣区域(ROI)的DTI指标分析表明，取决于ROI的大小及其在空间中的位置，校正方法可以在不同程度上消除系统偏差。提出并致力于这种类型的数据的预处理管道与BSD方法一起导致各向同性体模中的分数各向异性（FA）（全局和局部）降低了90％，在大鼠大脑中降低了45％。去噪后，与未进行预处理（sDTI）的标准方法相比，大鼠脑示踪图的总体变化最大。去噪后从DTI获得的第一个特征向量的方向，在去噪和吉布斯振铃去除后，ROI与sDTI和sDTI的Gibbs振铃去除和BSD平均相差56和10度，分别。由于消除了与不完美的磁场梯度有关的系统误差，因此可以通过纤维束造影的改进来识别后者。根据结果,高分辨率数据的系统偏差主要取决于信噪比，但是也可以看到非均匀梯度的影响。去噪之后,BSD方法能够进一步校正大鼠大脑中扩散张量的度量和纤维束成像，方法是考虑到磁场梯度的实际分布与被检查对象无关，并且唯一依赖于扫描仪和序列。这意味着体内研究也受到这种类型的错误，在处理此类数据时应考虑到这一点。
