Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.
Neuroimage. 2014 Feb 1;86:67-80. doi: 10.1016/j.neuroimage.2013.07.067. Epub 2013 Aug 5.
There is accumulating evidence that at current acquisition resolutions for diffusion-weighted (DW) MRI, the vast majority of white matter voxels contains "crossing fibers", referring to complex fiber configurations in which multiple and distinctly differently oriented fiber populations exist. Spherical deconvolution based techniques are appealing to characterize this DW intra-voxel signal heterogeneity, as they provide a balanced trade-off between constraints on the required hardware performance and acquisition time on the one hand, and the reliability of the reconstructed fiber orientation distribution function (fODF) on the other hand. Recent findings, however, suggest that an inaccurate calibration of the response function (RF), which represents the DW signal profile of a single fiber orientation, can lead to the detection of spurious fODF peaks which, in turn, can have a severe impact on tractography results. Currently, the computation of this RF is either model-based or estimated from selected voxels that have a fractional anisotropy (FA) value above a predefined threshold. For both approaches, however, there are user-defined settings that affect the RF and, consequently, fODF estimation and tractography. Moreover, these settings still rely on the second-rank diffusion tensor, which may not be the appropriate model, especially at high b-values. In this work, we circumvent these issues for RF calibration by excluding "crossing fibers" voxels in a recursive framework. Our approach is evaluated with simulations and applied to in vivo and ex vivo data sets with different acquisition settings. The results demonstrate that with the proposed method the RF can be calibrated in a robust and automated way without needing to define ad-hoc FA threshold settings. Our framework facilitates the use of spherical deconvolution approaches in data sets in which it is not straightforward to define RF settings a priori.
越来越多的证据表明，在当前扩散加权（DW）MRI 的采集分辨率下，绝大多数白质体素包含“交叉纤维”，指的是复杂的纤维结构，其中存在多个和明显不同方向的纤维群体。基于球谐分解的技术吸引人之处在于，它们在硬件性能要求和采集时间的约束与重建纤维方向分布函数（fODF）的可靠性之间提供了平衡的折衷。然而，最近的发现表明，响应函数（RF）的校准不准确，RF 代表单个纤维方向的 DW 信号轮廓，可能会导致虚假 fODF 峰的检测，而这反过来又会对轨迹追踪结果产生严重影响。目前，该 RF 的计算要么是基于模型的，要么是根据具有预设阈值以上的分数各向异性（FA）值的选定体素来估计的。然而，对于这两种方法，都存在影响 RF 的用户定义设置，从而影响 fODF 估计和轨迹追踪。此外，这些设置仍然依赖于二阶扩散张量，而该张量可能不是合适的模型，尤其是在高 b 值下。在这项工作中，我们通过在递归框架中排除“交叉纤维”体素来规避 RF 校准中的这些问题。我们的方法通过模拟进行了评估，并应用于具有不同采集设置的体内和离体数据集。结果表明，通过提出的方法，可以以稳健和自动化的方式校准 RF，而无需定义特定的 FA 阈值设置。我们的框架便于在难以事先定义 RF 设置的数据集上使用球谐分解方法。
