The accurate characterization of the diffusion process in tissue using diffusion MRI is greatly challenged by the presence of artefacts. Subject motion causes not only spatial misalignments between diffusion weighted images, but often also slicewise signal intensity errors. Voxelwise robust model estimation is commonly used to exclude intensity errors as outliers. Slicewise outliers, however, become distributed over multiple adjacent slices after image registration and transformation. This challenges outlier detection with voxelwise procedures due to partial volume effects. Detecting the outlier slices before any transformations are applied to diffusion weighted images is therefore required. In this work, we present i) an automated tool coined SOLID for slicewise outlier detection prior to geometrical image transformation, and ii) a framework to naturally interpret data uncertainty information from SOLID and include it as such in model estimators. SOLID uses a straightforward intensity metric, is independent of the choice of the diffusion MRI model, and can handle datasets with a few or irregularly distributed gradient directions. The SOLID-informed estimation framework prevents the need to completely reject diffusion weighted images or individual voxel measurements by downweighting measurements with their degree of uncertainty, thereby supporting convergence and well-conditioning of iterative estimation algorithms. In comprehensive simulation experiments, SOLID detects outliers with a high sensitivity and specificity, and can achieve higher or at least similar sensitivity and specificity compared to other tools that are based on more complex and time-consuming procedures for the scenarios investigated. SOLID was further validated on data from 54 neonatal subjects which were visually inspected for outlier slices with the interactive tool developed as part of this study, showing its potential to quickly highlight problematic volumes and slices in large population studies. The informed model estimation framework was evaluated both in simulations and in vivo human data.

译文

:伪影的存在极大地挑战了使用弥散MRI对组织中弥散过程的准确表征。对象运动不仅导致扩散加权图像之间的空间失准,而且通常还引起切片信号强度误差。体素鲁棒模型估计通常用于排除强度误差作为异常值。然而,在图像配准和变换之后,切片异常值变得分布在多个相邻切片上。由于部分体积的影响,这对使用体素程序进行离群值检测提出了挑战。因此需要在将任何变换应用于扩散加权图像之前检测离群切片。在这项工作中,我们提出了i)一种由SOLID创造的自动工具,用于在进行几何图像变换之前进行切片离群值检测,以及ii)一个自然解释SOLID的数据不确定性信息并将其包括在模型估计器中的框架。 SOLID使用简单的强度度量,独立于扩散MRI模型的选择,并且可以处理具有几个或不规则分布的梯度方向的数据集。 SOLID通知的估计框架通过降低不确定性程度的测量权重来防止完全拒绝扩散加权图像或单个体素测量的需要,从而支持迭代估计算法的收敛性和良好条件。在全面的模拟实验中,SOLID以较高的灵敏度和特异性检测异常值,并且与基于其他更复杂,更耗时的方案的工具相比,可以实现更高或至少相似的灵敏度和特异性。 SOLID在来自54个新生儿受试者的数据上进行了进一步验证,这些数据已通过本研究的一部分开发的交互式工具进行了视觉检查,以检测异常值,显示出其在大型人群研究中快速突出问题体积和切片的潜力。在模拟和体内人类数据中都评估了该知情的模型估计框架。

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