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

光声成像(PAI)是一种新兴的成像方式，它提供了非侵入性和实时测量光学组织特性的能力。这项技术最有前途的应用之一是评估功能性组织特性，如血液氧合。这种分析需要对几个波长的光吸收进行精确和空间分辨率的测量。然而，PAI的测量不仅依赖于光的吸收，还依赖于光的通量，即光在组织中的分布，这使得这一问题具有不适定性。因此，PAI的一个关键的计算挑战是从重建的光声图像中恢复组织潜在的吸收和散射属性的量化值。由于几乎不可能对体内潜在的光学组织属性进行地面真实测量，因此解决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很有前途的方法来解决这个问题，但目前的方法存在着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，可以利用新的数据驱动方法和先进的组织模拟模型的组合来解决这个问题。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拟体模的公式合作，以创建模拟和实验光声测量对。所获得的数据集可用于检验中心假设：(1)检查和量化模拟测量和实验测量之间的差距；(2)由于基本真实光学特性信息的可用性，以监督的方式训练实验数据的数据驱动模型；以及(3)研究将针对组织模拟模型训练的数据驱动反转算法应用于不同的体外和体内实验数据的可行性。

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.