Integrated deep learning model for personalized transcranial magnetic stimulation

用于个性化经颅磁刺激的集成深度学习模型

• 批准号: 22K12765
• 负责人: Rashed Essam
• 金额: $ 2.08万
• 依托单位: University of Hyogo
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2022
• 资助国家: 日本
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-22K12765/
• 关键词: Deep learning; Brain stimulation; TMS; Segmentation; brain stimulation

In this year, we have developed a new deep learning model for the segmentation of all head tissues using multi-modal MRI scans. The developed model segmented MRI scans into 16 different tissues. A dataset of multi-modal MRI (T1w, T2w, PD and MRA) are collected from 600 subjects. The images are pre-processed through bias-correction, registration and normalization to be used for human head dataset. A segmentation of 20 subjects are obtained through semi-automatic method and used to train the deep learning model. The developed method is applied to the remaining MRI dataset to generate segmented head models. After visual validation, a set of 196 fully segmented human head models with variabilities in gender and age was approved. The use of MRA leads to significant improvement of identification of brain vessels and arteries. This dataset will be used next year for large-scale TMS study. Initial TMS study was conducted using two subjects (through collaborators) to compute the induced electric field using different coil positions, orientation and location around motor cortex.Another deep learning model was developed for the estimation of the induced electric field in human brain directly from the anatomical images. Training of the new model is scheduled for next year.

今年，我们开发了一种新的深度学习模型，用于使用多模态MRI扫描分割所有头部组织。开发的模型将MRI扫描分割成16种不同的组织。收集了600名受试者的多模态MRI（T1w，T2w，PD和MRA）数据集。通过对图像进行偏差校正、配准和归一化等预处理，将其用于人体头部数据集。通过半自动方法获得20个主题的分割，并用于训练深度学习模型。所开发的方法应用于剩余的MRI数据集，以生成分割的头部模型。经过视觉验证，一组196个完全分割的人类头部模型，性别和年龄的变化被批准。MRA的使用导致脑血管和动脉的识别的显著改善。该数据集将于明年用于大规模TMS研究。最初的TMS研究由两名受试者（通过合作者）进行，使用不同的线圈位置、方向和运动皮层周围的位置来计算感应电场。另一种深度学习模型被开发用于直接从解剖图像估计人脑中的感应电场。新模型的训练定于明年进行。