Intelligent systems in interventional healthcare depend on the reliable perception of the environment. In this context, photoacoustic tomography (PAT) has emerged as a non-invasive, functional imaging modality with great clinical potential. Current research focuses on converting the high-dimensional, not human-interpretable spectral data into the underlying functional information, specifically the blood oxygenation. One of the largely unexplored issues stalling clinical advances is the fact that the quantification problem is ambiguous, i.e. that radically different tissue parameter configurations could lead to almost identical photoacoustic spectra. In the present work, we tackle this problem with conditional Invertible Neural Networks (cINNs). Going beyond traditional point estimates, our network is used to compute an approximation of the conditional posterior density of tissue parameters given the photoacoustic spectrum. To this end, an automatic mode detection algorithm extracts the plausible solution from the sample-based posterior. According to a comprehensive validation study based on both synthetic and real images, our approach is well-suited for exploring ambiguity in quantitative PAT.

中文翻译：

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使用条件可逆神经网络对组织氧合进行光声量化


介入医疗保健中的智能系统依赖于对环境的可靠感知。在这种情况下，光声断层扫描 （PAT） 已成为一种具有巨大临床潜力的非侵入性功能性成像方式。目前的研究重点是将高维的、人类无法解释的光谱数据转化为潜在的功能信息，特别是血氧。阻碍临床进展的基本上未探索的问题之一是量化问题是模棱两可的，即完全不同的组织参数配置可能导致几乎相同的光声光谱。在本工作中，我们使用条件可逆神经网络 （cINN） 来解决这个问题。超越传统的点估计，我们的网络用于计算给定光声光谱的组织条件后验密度的近似值。为此，自动模式检测算法从基于样本的后验中提取合理的解决方案。根据基于合成和真实图像的综合验证研究，我们的方法非常适合探索定量 PAT 中的模糊性。