Exploiting standardised tissue-mimicking phantoms to enable deep learning-based estimation of optical tissue properties on experimental photoacoustic data

光声成像（派）是一种新兴的成像方式，提供了非侵入性和实时测量光学组织特性的能力。该技术最有前途的应用之一是估计功能组织特性，如血氧。这种分析需要在几个波长下精确和空间分辨地测量光吸收。然而，派的测量不仅依赖于光的吸收，而且还依赖于能量密度，即光在组织中的分布，这使得问题具有不适定逆性质。因此，派的一个关键的计算挑战是从重建的光声图像中恢复组织的基本吸收和散射特性的定量值。由于实际上不可能在体内获得基础光学组织特性的地面实况测量，因此应对这一挑战的方法必须依赖于模拟数据。虽然已经提出了几种解决这个问题的有前途的方法，但目前的方法存在数值模型和实验数据之间的系统性差距，该项目的中心假设是，可以通过利用新的数据驱动方法和先进的组织模拟幻影相结合来解决这个问题。为此，最先进的物理前向模型将与最近开发的标准化组织模仿体模配方合作，以创建成对的模拟和实验光声测量。所获取的数据集可用于通过以下方式来测试中心假设：（1）检查和量化模拟测量与实验测量之间的差距;（2）由于地面实况光学性质信息的可用性，以监督方式在实验数据上训练数据驱动模型;以及（3）研究应用在组织上训练的数据驱动反演算法的可行性，模拟幻影不同的体外和体内实验数据。

Photoacoustic imaging (PAI) is an emerging imaging modality that offers the capability of measuring optical tissue properties non-invasively and in real-time. One of the most promising applications of the technique is the estimation of functional tissue properties, such as blood oxygenation. Such analysis requires the accurate and spatially resolved measurement of optical absorption at several wavelengths. However, PAI measurements are not only dependent on the optical absorption, but also the fluence, the distribution of light in tissue, which makes the problem of ill-posed inverse nature. As a result, a key computational challenge of PAI is the recovery of quantitative values of the underlying absorption and scattering properties of the tissue from reconstructed photoacoustic images. As it is practically impossible to obain ground truth measurements of the underlying optical tissue properties in vivo, methods that tackle this challenge have to rely on simulated data. While several promising approaches that tackle this problem have already been presented, current methods suffer from a systematic gap between numerical models and experimental data.The central hypothesis of this project is that the problem can be tackled by leveraging a combination of novel data-driven approaches and advanced tissue-mimicking phantoms. To this end, state-of-the-art physical forward models will be partnered with a recently developed formulation for standardised tissue-mimicking phantoms to create pairs of simulated and experimental photoacoustic measurements. The acquired dataset can be used to test the central hypothesis by: (1) examining and quantifying the gap between simulated and experimental measurements; (2) training data-driven models on experimental data in a supervised manner due to the availability of ground truth optical property information; and (3) investigating the feasibility of applying the data-driven inversion algorithm trained on tissue-mimicking phantoms to different in vitro and in vivo experimental data.