一种对断层扫描和附加状态方法的概率方法,在医学超声波中应用到全波形反转
Oscar Bates1, Lluis Guasch2, George Strong2
1Department of Bioengineering, Imperial College London, SW7 2AZ, United Kingdom.
Inverse problems
|August 22, 2024
概括
随机变量推理 (SVI) 提供了一种计算效率高的方法来解决反向问题. 这项研究证明了SVI.
科学领域:
- 计算物理学的计算物理.
- 应用数学 应用数学 应用数学
- 医学成像医学成像
背景情况:
- 贝叶斯推理对于解决复杂的反向问题至关重要.
- 贝叶斯方程的传统方法可能是计算密集的.
- 随机变化推理 (SVI) 提供了一个基于梯度的替代方案.
研究的目的:
- 评估SVI对反向问题的应用,特别是在时间有限或计算昂贵的场景中.
- 在超声波脑断层扫描中使用全波形反转 (FWI) 应用SVI.
- 证明SVI在重建中估计不确定性的实用性.
主要方法:
- 利用随机变化推理 (SVI) 来解决贝叶斯方程.
- 应用SVI到全波形逆转 (FWI) 进行超声脑断层扫描.
- 采用平均场高斯近似来进行方差估计.
主要成果:
- SVI成功地提供了无成本估计音速分布中的像素智能变异.
- 使用SVI.证明了估计声速重建不确定性的实验可行性.
- 差异估计器的计算成本可以忽略不计.
结论:
- 在像FWI这样的反向问题中,SVI是一种可行的和有效的不确定性量化方法.
- 估计的不确定性可以用于对断层扫描的图像质量评估.
- 低成本的差异估计器在医学成像之外具有广泛的应用,包括工程领域.
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