Poimala Jenni, Cox Ben, Hauptmann Andreas
Research Unit of Mathematical Sciences, University of Oulu, Finland.
Department of Medical Physics and Biomedical Engineering, University College London, UK.
Photoacoustics. 2024 Feb 17;37:100597. doi: 10.1016/j.pacs.2024.100597. eCollection 2024 Jun.
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.
基于平面传感器的三维光声层析成像中的实时应用依赖于快速重建算法，这些算法假定组织中的声速（SoS）是均匀的。此外，重建质量取决于对恒定声速的正确选择。在本研究中，我们讨论了通过使用学习型重建方法改善未知或非均匀声速分布问题的可能性。这可以通过对训练数据中的不确定性进行建模来实现。此外，在学习型重建方法中可以包含一个校正项。我们研究了两者的影响，虽然学习型校正组件可以进一步提高重建质量，但我们表明，在训练数据中仔细选择不确定性是克服未知声速的主要因素。我们通过三维模拟和测量来支持我们的发现。
