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Whole-Brain 3D Activation and Functional Connectivity Mapping in Mice using Transcranial Functional Ultrasound Imaging

Whole-Brain 3D Activation and Functional Connectivity Mapping in Mice using Transcranial Functional Ultrasound Imaging

Published on: February 24, 2021

基于完全卷积网络的深度学习的3D超声波脑成像.

Jiahao Ren¹, Xiaocen Wang¹, Chang Liu¹

¹State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China.

Sensors (Basel, Switzerland)

|October 14, 2023

概括

这项研究引入了一种3D人工智能算法,用于精确的脑超声波重建,克服了头骨的局限性. 脑成像全卷积网络 (BIFCN) 为脑成像提供了一个更快,更安全的替代方案.

关键词:

大脑图像重建脑图像重建机器学习是机器学习.实时成像成像是可以实现的.超声波超声波是指超声波的使用.

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科学领域:

医疗成像医学成像
人工智能的人工智能
生物医学工程生物医学工程

背景情况:

超声波成像与MRI和CT相比具有优势,但由于头骨声阻抗,在成年人脑成像中面临挑战.
传统的超声波难以穿透头骨,限制了它在脑部成像中的应用.

研究的目的:

开发和验证一个3D人工智能算法,用于精确的跨脑脑超声波重建.
为了克服传统超声波在成年人大脑成像中的局限性.

主要方法:

一个3D人工智能算法,大脑成像全卷积网络 (BIFCN),被开发,整合波形建模和深度学习.
BIFCN网络架构包括一个输入层,四个卷积层和一个用于培训的聚合层.
该算法使用模拟实验和实验室重建以纯水为初始模型进行了测试.

主要成果:

模拟实验显示了重建和真实大脑图像之间的高皮尔森相关系数.
实验室结果显示了令人印象深刻的3D重建精度,即使没有事先的信息.
3D BIFCN 网络在 8 小时内训练,并在 12.67 秒内重建 10 个样本,表明高效率.

结论:

3D BIFCN算法提供了使用超声波进行3D脑成像的准确和高效方法.
这种人工智能驱动的方法可以快速准确地对脑组织进行成像,将波场数据映射到3D模型中.
在血液中观察到的频率转移现象可能为使用BIFCN的全脑血液成像提供新的定量见解.