BACKGROUND Transcranial magnetic stimulation (TMS) plays an important role in treatment of mental and neurological illnesses, and neurosurgery. However, it is difficult to target specific brain regions accurately because the complex anatomy of the brain substantially affects the shape and strength of the electric fields induced by the TMS coil. A volume conductor model can be used for determining the accurate electric fields; however, the construction of subject-specific anatomical head structures is time-consuming. OBJECTIVE The aim of this study is to propose a method to estimate electric fields induced by TMS from only T1 magnetic resonance (MR) images, without constructing a subject-specific anatomical model. METHODS Very large sets of electric fields in the brain of subject-specific anatomical models, which are constructed from T1 and T2 MR images, are computed by a volume conductor model. The relation between electric field distribution and T1 MR images is used for machine learning. Deep neural network (DNN) models are applied for the first time to electric field estimation. RESULTS By determining the relationships between the T1 MR images and electric fields by DNN models, the process of electric field estimation is markedly accelerated (to 0.03 s) due to the absence of a requirement for anatomical head structure reconstruction and volume conductor computation. Validation shows promising estimation accuracy, and rapid computations of the DNN model are apt for practical applications. CONCLUSION The study showed that the DNN model can estimate the electric fields from only T1 MR images and requires low computation time, suggesting the possibility of using machine learning for real-time electric field estimation in navigated TMS.

中文翻译：

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使用深度神经网络实时估计由经颅磁刺激引起的电场

背景技术经颅磁刺激(TMS)在精神和神经疾病的治疗以及神经外科手术中起着重要作用。然而，由于大脑的复杂解剖结构会极大地影响 TMS 线圈感应的电场的形状和强度，因此很难准确定位特定的大脑区域。体积导体模型可用于确定精确的电场；然而，构建特定主题的头部解剖结构非常耗时。目的 本研究的目的是提出一种仅从 T1 磁共振 (MR) 图像估计 TMS 感应电场的方法，而无需构建特定于受试者的解剖模型。方法 在特定主题的解剖模型的大脑中设置非常大的电场，由 T1 和 T2 MR 图像构成，由体积导体模型计算。电场分布和 T1 MR 图像之间的关系用于机器学习。深度神经网络 (DNN) 模型首次应用于电场估计。结果 通过 DNN 模型确定 T1 MR 图像与电场之间的关系，由于不需要解剖头部结构重建和体积导体计算，电场估计过程显着加快（至 0.03 s）。验证显示出有希望的估计精度，并且 DNN 模型的快速计算适合实际应用。结论 研究表明，DNN 模型可以仅从 T1 MR 图像估计电场，并且计算时间较短，