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

利用标准化的组织模仿体模，实现基于实验光声数据的光学组织特性的基于深度学习的估计

• 批准号: 458342884
• 负责人: Dr. Janek Gröhl
• 金额: --
• 依托单位: Li Ka Shing Centre
• 依托单位国家: 德国
• 项目类别: WBP Fellowship
• 财政年份: 2021
• 资助国家: 德国
• 起止时间: 2020-12-31 至 无数据
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/458342884?language=en
• 关键词: Exploiting; standardised; tissue; mimicking; phantoms

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.

光声成像（PAI）是一种新兴的成像方式，提供了无创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的测量。然而，PAI测量不仅依赖于光吸收，还依赖于光在组织中的分布，这使得问题具有不适定逆的性质。因此，PAI的一个关键计算挑战是从重建的光声图像中恢复组织的底层吸收和散射特性的定量值。由于几乎不可能获得体内潜在光学组织特性的地面真实测量，因此解决这一挑战的方法必须依赖于模拟数据。虽然已经提出了一些解决这一问题的有希望的方法，但目前的方法在数值模型和实验数据之间存在系统性差距。这个项目的核心假设是，这个问题可以通过利用新颖的数据驱动方法和先进的组织模拟模型的结合来解决。为此，最先进的物理正演模型将与最近开发的标准化组织模拟幻影配方合作，以创建模拟和实验光声测量对。获取的数据集可以通过以下方式来检验中心假设：(1)检查和量化模拟和实验测量之间的差距；(2)利用地真光学属性信息的可用性，以监督的方式训练实验数据上的数据驱动模型；(3)研究将模拟组织模型训练的数据驱动反演算法应用于不同的体外和体内实验数据的可行性。