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.

人脑的磁共振电流密度成像 (MRCDI) 旨在通过基于 MR 的电流感应磁场测量来重建由经颅电刺激引起的电流密度分布。到目前为止，MRCDI 数据采集仅实现了低信噪比，不能提供全体积覆盖，并且缺乏来自头皮和颅骨区域的数据。此外，它只对平行于扫描仪场的电流感应磁场分量敏感。因此，重建问题涉及处理嘈杂和不完整的数据，这使得它在数学上具有挑战性。大多数现有的重建方法已经通过模拟研究和在具有简化几何形状的模型中的测量得到验证。只有一种重建方法，投影电流密度算法，

我们分析了投影电流密度算法在应用于人脑数据时性能有限的根本原因。此外，我们将其与依赖于优化从结构 MR 数据重建的解剖详细头部模型的少量组织隔室的电导率的方法进行比较。对于模拟的地面实况数据和人类体内 MRCDI 数据，我们的结果表明，使用个性化的体积导体模型比应用投影电流密度算法更有利于电流密度的估计。特别是，我们引入了一种分层统计测试方法，作为测试和比较重建电流密度图像质量的一种原则方法，该方法解释了人类体内 MRCDI 数据的有限信噪比以及测量数据的电流密度未知。我们的结果表明，统计测试方法为进一步开发精确的头部体积导体模型构成了一个有价值的框架。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。我们的结果表明，统计测试方法为进一步开发精确的头部体积导体模型构成了一个有价值的框架。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。我们的结果表明，统计测试方法为进一步开发精确的头部体积导体模型构成了一个有价值的框架。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。