发表机构
Tampere University（坦佩雷大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出将无纤维束示踪的DTI数据融入卡尔曼滤波器，以增强脑电成像，通过高效算法推断脑区连接，实验证明能获取脑干和丘脑深层活动并限制扩散范围。
AI 中文摘要
这项概念验证论文展示了如何将卡尔曼滤波器作为一种脑成像方法，通过纳入无纤维束示踪的原始扩散张量成像（DTI）数据以及脑电图（EEG）记录来增强其性能。设计了一种高效算法，以简化从DTI数据推断脑区之间连接性的繁琐过程。基于DTI的演化模型被应用于卡尔曼滤波器和标准化卡尔曼滤波器，并将它们与使用随机游走演化模型的传统对应方法进行比较。数值实验采用合成的体感和听觉诱发电位。结果表明，DTI建模的卡尔曼滤波器能够获取脑干和丘脑中的深层活动。此外，连接模型将估计的扩散范围限制在正确或最近的解剖脑区内。
英文摘要
This proof-of-concept paper demonstrates how the Kalman filter, as a brain imaging method, can be enhanced by incorporating raw diffusion tensor imaging (DTI) data without tractography, in addition to EEG recordings. An efficient algorithm is designed to streamline the otherwise tedious process of inferring connectivity between brain regions from DTI data. The DTI-based evolution model is applied to the Kalman filter and the Standardized Kalman filter, and these are compared with their conventional counterparts using the random walk evolution model. The numerical experiments use synthetic somatosensory and auditory evoked potentials. The results show that the DTI-modelled Kalman filter can obtain deep activity in the brainstem and thalamus. Moreover, the connection model limits the estimated spreads to the correct or nearest anatomical brain region.
Comments24 pages, 7 figures
