Research Center for Motor Control and Neuroplasticity, KU Leuven, Tervuursevest 101, 3001, Leuven, Belgium.
Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy.
Neuroinformatics. 2021 Oct;19(4):585-596. doi: 10.1007/s12021-020-09504-5. Epub 2021 Jan 27.
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.
在过去的几年中，用于分析脑电图 (EEG) 记录的技术进步使得可以以前所未有的精度和可靠性研究人类大脑中的神经活动和连接。准确的 EEG 源重建的关键要素是构建一个现实的头部模型，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，这是一种用于从结构磁共振 (MR) 图像进行头部组织建模的工具包。该工具包由三个模块组成：1）图像预处理-对原始 MR 图像进行去噪并为进一步分析做准备；2）组织概率映射-从 MR 图像生成个体空间中的模板组织概率图 (TPM)；3）组织分割-整合来自所有 TPM 的信息，使得 MR 图像中的每个体素都被分配到特定的组织。MR-TIM 生成高度逼真的 3D 掩模，其中五个与大脑结构（脑和小脑灰质、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）相关。我们在健康志愿者和患者的 MR 图像以及来自开源存储库的 MR 模板图像上进行的验证表明，MR-TIM 比全头组织分割的替代方法更准确。我们希望，通过提高头部建模的精度，MR-TIM 将有助于更广泛地将 EEG 用作脑成像技术。
