在实体中验证的解剖意识深度学习,用于实时电场模拟
Liang Ma1,2, Gangliang Zhong2, Zhengyi Yang2
1School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
Journal of neural engineering
|November 8, 2023
概括
一个新的深度学习网络准确地模拟电场用于跨磁刺激 (TMS),大大减少了线圈放置时间. 这一进步使得心理疾病的TMS治疗更快,更个性化.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 医疗成像医学成像
背景情况:
- 跨磁刺激 (TMS) 是一种关键的非侵入性大脑调制技术,用于心理健康障碍.
- 精确的电场 (E-fields) 模拟对于有效的,有针对性的TMS至关重要.
- 目前的电子场模拟方法耗时且复杂,阻碍了快速的线圈放置优化.
研究的目的:
- 开发一个高效和准确的深度学习模型来模拟TMS诱导的E字段.
- 为了提高确定最佳TMS线圈位置的速度和精度.
- 为了促进个性化的TMS治疗策略.
主要方法:
- 一个注意力深度学习网络被设计用于模拟E-fields.
- 该网络使用个人MRI扫描和线圈配置作为输入.
- 它将MRI数据转化为明确的大脑组织,以产生局部E场分布.
主要成果:
- 与之前的深度学习方法相比,深度学习方法减少了灰色质E场强度的平均相对误差21.1%.
- 区域E场强度与电生理反应之间的相关性增加了35.0%.
- 活体TMS实验显示了与传统计算方法相比的刺激性能,将优化时间从几个小时减少到几分钟.
结论:
- 开发的深度学习网络提供了一个精确和有效的方法来模拟TMS E-fields.
- 这种方法显著加快了优化TMS线圈放置的速度.
- 该方法显示出强大的潜力,可以在临床环境中实现个性化的线圈放置,以进行个性化的TMS治疗.
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