Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark; Section for Magnetic Resonance, DTU Health Tech, Technical University of Denmark, Kgs Lyngby, Denmark.
Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark.
Neuroimage. 2021 Nov;243:118517. doi: 10.1016/j.neuroimage.2021.118517. Epub 2021 Sep 1.
Magnetic resonance current density imaging (MRCDI) of the human brain aims to reconstruct the current density distribution caused by transcranial electric stimulation from MR-based measurements of the current-induced magnetic fields. So far, the MRCDI data acquisition achieves only a low signal-to-noise ratio, does not provide a full volume coverage and lacks data from the scalp and skull regions. In addition, it is only sensitive to the component of the current-induced magnetic field parallel to the scanner field. The reconstruction problem thus involves coping with noisy and incomplete data, which makes it mathematically challenging. Most existing reconstruction methods have been validated using simulation studies and measurements in phantoms with simplified geometries. Only one reconstruction method, the projected current density algorithm, has been applied to human in-vivo data so far, however resulting in blurred current density estimates even when applied to noise-free simulated data. We analyze the underlying causes for the limited performance of the projected current density algorithm when applied to human brain data. In addition, we compare it with an approach that relies on the optimization of the conductivities of a small number of tissue compartments of anatomically detailed head models reconstructed from structural MR data. Both for simulated ground truth data and human in-vivo MRCDI data, our results indicate that the estimation of current densities benefits more from using a personalized volume conductor model than from applying the projected current density algorithm. In particular, we introduce a hierarchical statistical testing approach as a principled way to test and compare the quality of reconstructed current density images that accounts for the limited signal-to-noise ratio of the human in-vivo MRCDI data and the fact that the ground truth of the current density is unknown for measured data. Our results indicate that the statistical testing approach constitutes a valuable framework for the further development of accurate volume conductor models of the head. Our findings also highlight the importance of tailoring the reconstruction approaches to the quality and specific properties of the available data.
磁共振电流密度成像（MRCDI）旨在重建经颅电刺激引起的电流密度分布，其方法是从基于磁共振的电流感应磁场测量中获取数据。到目前为止，MRCDI 数据采集的信噪比很低，无法提供全容积覆盖，并且缺乏头皮和颅骨区域的数据。此外，它仅对与扫描仪场平行的电流感应磁场分量敏感。因此，重建问题涉及处理噪声和不完整的数据，这使得数学上具有挑战性。大多数现有的重建方法已经使用模拟研究和简化几何形状的体模中的测量进行了验证。到目前为止，只有一种重建方法，即投影电流密度算法，已应用于人体体内数据，但即使应用于无噪声的模拟数据，也会导致电流密度估计模糊。我们分析了在将投影电流密度算法应用于人脑数据时性能有限的根本原因。此外，我们将其与一种方法进行了比较，该方法依赖于从结构磁共振数据重建的解剖详细头部模型的少数组织隔室的电导率优化。对于模拟的真实数据和人体体内 MRCDI 数据，我们的结果均表明，与应用投影电流密度算法相比，使用个性化容积导体模型对电流密度的估计更有益。特别是，我们引入了一种分层统计检验方法，作为一种有原则的方法来测试和比较重建电流密度图像的质量，该方法考虑了人体体内 MRCDI 数据的信噪比有限以及测量数据中电流密度的真实值未知的事实。我们的结果表明，统计检验方法构成了对头的精确容积导体模型进一步发展的有价值框架。我们的研究结果还强调了根据可用数据的质量和特定属性定制重建方法的重要性。
