循环和卷积神经网络用于顺序多谱光声学断层扫描 (MSOT) 成像
Aniwat Juhong1,2, Bo Li1,2, Yifan Liu1,2
1Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan, USA.
Journal of biophotonics
|June 29, 2023
概括
一种新的深度学习模型显著加速了多谱光声学断层扫描 (MSOT) 成像. 这种先进的技术可以减少71%的扫描时间,使生物样本的分析速度更快,具有高度解剖细节.
科学领域:
- 生物医学成像技术 生物医学成像技术
- 医疗技术 医疗技术 医学技术
- 人工智能在医学中的应用
背景情况:
- 多谱光声学断层扫描 (MSOT) 为生物样本提供了详细的解剖学和生理学见解.
- 获取高分辨率的体积MSOT数据目前是一个耗时的过程.
研究的目的:
- 开发一个深度学习模型来加速体积MSOT图像采集.
- 为了减少获得高透平分辨率MSOT数据所需的时间.
主要方法:
- 一种混合循环和卷积神经网络模型被设计用于序列图像生成.
- 该模型处理两个稀疏采样的MSOT图像 (0.6毫米步骤大小) 来生成密集采样图像 (0.1毫米步骤大小).
- 在多式成像系统中,印ocyanine绿色结合纳米 (NWs-ICG) 被用作对比剂.
主要成果:
- 深度学习模型成功地在两个输入图像之间生成了五个中间截面图像.
- 与传统方法相比,这种方法将MSOT获取时间缩短了约71%.
- 该系统将MSOT,超声波和光声学成像在一个扫描中集成在一起.
结论:
- 拟议的深度学习模型显著加速体积MSOT图像采集.
- 这种方法提高了使用MSOT分析生物样本的效率.
- 混合神经网络方法为耗时的体积成像挑战提供了一个有希望的解决方案.
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