We introduce a physics-based computational reconstruction framework for non-invasive photoacoustic tomography through a thick aberrating layer. Our wave-based approach leverages an analytic formulation of diffraction to beamform a photoacoustic image, when the aberrating layer profile is known. When the profile of the aberrating layer is unknown, the same analytical formulation serves as the basis for an automatic-differentiation regularized optimization algorithm that simultaneously reconstructs both the profile of the aberrating layer and the optically absorbing targets. Results from numerical studies and proof-of-concept experiments show promise for fast beamforming that takes into account diffraction effects occurring in the propagation through thick, highly-aberrating layers.

我们引入了一种基于物理的计算重建框架，用于通过厚像差层进行非侵入性光声断层扫描。当像差层轮廓已知时，我们基于波的方法利用衍射的分析公式来波束形成光声图像。当像差层的轮廓未知时，相同的解析公式可作为自动微分正则化优化算法的基础，该算法同时重建像差层和光学吸收目标的轮廓。数值研究和概念验证实验的结果显示了快速波束形成的前景，该波束形成考虑了在厚的高像差层传播中发生的衍射效应。