Martin-Fernandez M, Muñoz-Moreno E, Cammoun L, Thiran J-P, Westin C-F, Alberola-López C
Laboratorio de Procesado de Imagen (LPI), Universidad de Valladolid, Valladolid, Spain.
Med Image Anal. 2009 Feb;13(1):19-35. doi: 10.1016/j.media.2008.05.004. Epub 2008 Jun 7.
It has been shown that the tensor calculation is very sensitive to the presence of noise in the acquired images, yielding to very low quality Diffusion Tensor Images (DTI) data. Recent investigations have shown that the noise present in the Diffusion Weighted Images (DWI) causes bias effects on the DTI data which cannot be corrected if the noise characteristic is not taken into account. One possible solution is to increase the minimum number of acquired measurements (which is 7) to several tens (or even several hundreds). This has the disadvantage of increasing the acquisition time by one (or two) orders of magnitude, making the process inconvenient for a clinical setting. We here proposed a turn-around procedure for which the number of acquisitions is maintained but, the DWI data are filtered prior to determining the DTI. We show a significant reduction on the DTI bias by means of a simple and fast procedure which is based on linear filtering; well-known drawbacks of such filters are circumvented by means of anisotropic neighborhoods and sequential application of the filter itself. Information of the first order probability density function of the raw data, namely, the Rice distribution, is also included. Results are shown both for synthetic and real datasets. Some error measurements are determined in the synthetic experiments, showing how the proposed scheme is able to reduce them. It is worth noting a 50% increase in the linear component for real DTI data, meaning that the bias in the DTI is considerably reduced. A novel fiber smoothness measure is defined to evaluate the resulting tractography for real DWI data. Our findings show that after filtering, fibers are considerably smoother on the average. Execution times are very low as compared to other reported approaches which allows for a real-time implementation.
研究表明，张量计算对采集图像中的噪声非常敏感，会产生质量很低的扩散张量图像（DTI）数据。最近的研究表明，扩散加权图像（DWI）中存在的噪声会对DTI数据产生偏差效应，如果不考虑噪声特征，这种偏差就无法校正。一种可能的解决方案是将采集测量的最小数量（7次）增加到几十次（甚至几百次）。这样做的缺点是采集时间增加了一（或两）个数量级，使得该过程在临床环境中不方便使用。我们在此提出了一种改进方法，保持采集次数不变，但在确定DTI之前对DWI数据进行滤波。我们通过一种基于线性滤波的简单快速方法，显著降低了DTI偏差；通过各向异性邻域和滤波器本身的顺序应用，规避了此类滤波器众所周知的缺点。还纳入了原始数据一阶概率密度函数的信息，即莱斯分布。给出了合成数据集和真实数据集的结果。在合成实验中确定了一些误差测量值，展示了所提方案如何能够降低这些误差。值得注意的是，真实DTI数据的线性分量增加了50%，这意味着DTI中的偏差大幅降低。定义了一种新颖的纤维平滑度测量方法，以评估真实DWI数据生成的纤维束成像。我们的研究结果表明，滤波后，纤维平均而言更加平滑。与其他报道的方法相比，执行时间非常短，这使得能够实时实现。
