Conventional photoacoustic imaging may suffer from the limited view and bandwidth of ultrasound transducers. A deep learning approach is proposed to handle these problems and is demonstrated both in simulations and in experiments on a multi-scale model of leaf skeleton. We employed an experimental approach to build the training and the test sets using photographs of the samples as ground truth images. Reconstructions produced by the neural network show a greatly improved image quality as compared to conventional approaches. In addition, this work aimed at quantifying the reliability of the neural network predictions. To achieve this, the dropout Monte-Carlo procedure is applied to estimate a pixel-wise degree of confidence on each predicted picture. Last, we address the possibility to use transfer learning with simulated data in order to drastically limit the size of the experimental dataset.

传统的光声成像可能会受到超声换能器的有限视野和带宽的影响。提出了一种深度学习方法来处理这些问题，并在叶骨架的多尺度模型的模拟和实验中得到了证明。我们采用实验方法来使用样本照片作为地面实况图像来构建训练和测试集。与传统方法相比，神经网络产生的重建显示图像质量大大提高。此外，这项工作旨在量化神经网络预测的可靠性。为了实现这一点，应用了 dropout Monte-Carlo 过程来估计每个预测图片的像素级置信度。最后，我们讨论了将迁移学习与模拟数据结合使用的可能性，以极大地限制实验数据集的大小。