In the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1) image pre-processing – the raw MR image is denoised and prepared for further analyses; 2) tissue probability mapping – template tissue probability maps (TPMs) in individual space are generated from the MR image; 3) tissue segmentation – information from all the TPMs is integrated such that each voxel in the MR image is assigned to a specific tissue. MR-TIM generates highly realistic 3D masks, five of which are associated with brain structures (brain and cerebellar grey matter, brain and cerebellar white matter, and brainstem) and the remaining seven with other head tissues (cerebrospinal fluid, spongy and compact bones, eyes, muscle, fat and skin). Our validation, conducted on MR images collected in healthy volunteers and patients as well as an MR template image from an open-source repository, demonstrates that MR-TIM is more accurate than alternative approaches for whole-head tissue segmentation. We hope that MR-TIM, by yielding an increased precision in head modelling, will contribute to a more widespread use of EEG as a brain imaging technique.

在过去的几年里，脑电图 (EEG) 记录分析技术的进步已经允许以前所未有的精度和可靠性研究人脑中的神经活动和连通性。准确脑电图源重建的一个关键要素是构建逼真的头部模型，其中包含有关电极位置和头部组织分布的信息。在本文中，我们介绍了 MR-TIM，这是一个从结构磁共振 (MR) 图像中进行头部组织建模的工具箱。该工具箱由三个模块组成： 1)图像预处理——原始 MR 图像被去噪并为进一步分析做准备；2)组织概率图——个体空间中的模板组织概率图 (TPM) 从 MR 图像中生成；3)组织分割– 整合来自所有 TPM 的信息，以便将 MR 图像中的每个体素分配给特定组织。MR-TIM 生成高度逼真的 3D 面具，其中五个与大脑结构（大脑和小脑灰质、大脑和小脑白质以及脑干）相关，其余七个与其他头部组织（脑脊液、海绵状和致密骨、眼睛、肌肉、脂肪和皮肤）。我们对健康志愿者和患者收集的 MR 图像以及来自开源存储库的 MR 模板图像进行了验证，表明 MR-TIM 比用于全头组织分割的替代方法更准确。我们希望 MR-TIM 通过提高头部建模的精度，将有助于更广泛地使用脑电图作为脑成像技术。