Real-time applications in three-dimensional photoacoustic tomography from planar sensors rely on fast reconstruction algorithms that assume the speed of sound (SoS) in the tissue is homogeneous. Moreover, the reconstruction quality depends on the correct choice for the constant SoS. In this study, we discuss the possibility of ameliorating the problem of unknown or heterogeneous SoS distributions by using learned reconstruction methods. This can be done by modelling the uncertainties in the training data. In addition, a correction term can be included in the learned reconstruction method. We investigate the influence of both and while a learned correction component can improve reconstruction quality further, we show that a careful choice of uncertainties in the training data is the primary factor to overcome unknown SoS. We support our findings with simulated and measurements in 3D.

中文翻译：

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补偿学习快速 3D 有限视角光声断层扫描中未知的声速

平面传感器三维光声断层扫描的实时应用依赖于快速重建算法，该算法假设组织中的声速 (SoS) 是均匀的。此外，重建质量取决于常数SoS的正确选择。在本研究中，我们讨论了通过使用学习的重建方法来改善未知或异构 SoS 分布问题的可能性。这可以通过对训练数据中的不确定性进行建模来完成。另外，校正项可以包含在学习的重建方法中。我们研究了两者的影响，虽然学习的校正分量可以进一步提高重建质量，但我们表明，仔细选择训练数据中的不确定性是克服未知 SoS 的主要因素。我们通过 3D 模拟和测量来支持我们的发现。