Multi-scale anatomical awareness improves the accuracy of the real-time electric field estimation | |
Ma L(马亮)1,2; Zhong GL(钟刚亮)1; Yang ZY(杨正宜)1; Fan LZ(樊令仲)1; Jiang TZ(蒋田仔)1 | |
2021 | |
会议日期 | 2021-7 |
会议地点 | Shenzhen,China |
关键词 | deep regression model anatomical awareness real-time, electric field estimation transcranial magnetic stimulation |
页码 | 1-7 |
英文摘要 | Abstract—Induced electric fields (E-field) of a coil within target areas are substantial for precise transcranial therapy. The fast and precise estimation of a stimulation is essential for a navigation system. However, high accuracy and low time consumption are rarely satisfied at the same time in previous models. In this paper, we present an anatomical-awareness model to integrate binary, explicit anatomical structures as mediate variables. Multi-scale attention blocks are also introduced to capture the anatomical variations. The presented model mitigates the anatomy-related errors. The presented architecture not only reduces the mean relative errors of E-field to about 7%, but also has the characteristics of low time consumption, which makes it suitable for a real-time navigation system. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/50752] |
专题 | 自动化研究所_脑网络组研究中心 |
通讯作者 | Jiang TZ(蒋田仔) |
作者单位 | 1.Brainnetome Center, Institute of Automation Chinese Academy of Sciences 2.School of Artificial Intelligence University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Ma L,Zhong GL,Yang ZY,et al. Multi-scale anatomical awareness improves the accuracy of the real-time electric field estimation[C]. 见:. Shenzhen,China. 2021-7. |
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