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首页> 外文期刊>NeuroImage >Recursive calibration of the fiber response function for spherical deconvolution of diffusion MRI data
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Recursive calibration of the fiber response function for spherical deconvolution of diffusion MRI data

机译：纤维响应函数的递归校准，用于弥散MRI数据的球形反褶积

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摘要

There is accumulating evidence that at current acquisition resolutions for diffusion-weighted (DW) MRI, the vast majority of white matter voxels contains ~(11)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 ~(11)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.
机译：越来越多的证据表明，在当前的扩散加权（DW）MRI采集分辨率下，绝大多数白质体素都包含〜（11）交叉纤维”，这是指其中存在多个且取向明显不同的纤维群的复杂纤维构型。基于球面反卷积的技术吸引人之处在于表征了这种DW内部体素信号的异质性，因为它们一方面在所需硬件性能的约束和采集时间之间的平衡与重构的光纤方向分布函数的可靠性之间取得了平衡的权衡（然而，最近的发现表明，响应函数（RF）的不正确校准（代表单根光纤方向的DW信号曲线）可能导致检测到伪造的fODF峰，进而导致会严重影响超声检查结果。目前，此RF的计算是基于模型的或估计的从具有高于预定阈值的分数各向异性（FA）值的选定体素中选择。但是，对于这两种方法，都有用户定义的设置，这些设置会影响RF，进而影响fODF估计和束线描记。而且，这些设置仍然依赖于第二级扩散张量，该张量可能不是合适的模型，尤其是在高b值的情况下。在这项工作中，我们通过在递归框架中排除〜（11）交叉纤维“体素，从而避免了RF校准的这些问题。我们的方法经过仿真评估，并应用于具有不同采集设置的体内和离体数据集。借助所提出的方法，可以以鲁棒性和自动化的方式校准RF，而无需定义临时FA阈值设置，我们的框架便于在数据集中使用球形反卷积方法，而在这些数据集中很难先验地定义RF设置。

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