Conventional reconstruction methods for photoacoustic images are not suitable for the scenario of sparse sensing and geometrical limitation. To overcome these challenges and enhance the quality of reconstruction, several learning-based methods have recently been introduced for photoacoustic tomography reconstruction. The goal of this study is to compare and systematically evaluate the recently proposed learning-based methods and modified networks for photoacoustic image reconstruction. Specifically, learning-based post-processing methods and model-based learned iterative reconstruction methods are investigated. In addition to comparing the differences inherently brought by the models, we also study the impact of different inputs on the reconstruction effect. Our results demonstrate that the reconstruction performance mainly stems from the effective amount of information carried by the input. The inherent difference of the models based on the learning-based post-processing method does not provide a significant difference in photoacoustic image reconstruction. Furthermore, the results indicate that the model-based learned iterative reconstruction method outperforms all other learning-based post-processing methods in terms of generalizability and robustness.

用于光声图像的常规重建方法不适合稀疏感测和几何限制的情况。为了克服这些挑战并提高重建质量，最近已引入了几种基于学习的方法来进行光声层析成像重建。这项研究的目的是比较和系统地评估最近提出的基于学习的方法和光声图像重建的改进网络。具体来说，研究了基于学习的后处理方法和基于模型的学习迭代重建方法。除了比较模型固有的差异外，我们还研究了不同输入对重建效果的影响。我们的结果表明，重建性能主要来自输入所携带的有效信息量。基于基于学习的后处理方法的模型的固有差异不会在光声图像重建中提供显着差异。此外，结果表明，基于模型的学习迭代重建方法在可推广性和鲁棒性方面优于所有其他基于学习的后处理方法。