Godefroy Guillaume, Arnal Bastien, Bossy Emmanuel
Univ. Grenoble Alpes, CNRS, LIPhy, 38000 Grenoble, France.
Photoacoustics. 2020 Oct 27;21:100218. doi: 10.1016/j.pacs.2020.100218. eCollection 2021 Mar.
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.
传统光声成像可能会受到超声换能器有限视野和带宽的影响。本文提出了一种深度学习方法来解决这些问题，并在叶片骨架多尺度模型的模拟和实验中进行了验证。我们采用实验方法，以样本照片作为真实图像来构建训练集和测试集。与传统方法相比，神经网络生成的重建图像质量有了显著提高。此外，这项工作旨在量化神经网络预测的可靠性。为此，应用随机失活蒙特卡洛方法来估计每张预测图像上每个像素的置信度。最后，我们探讨了使用迁移学习和模拟数据以大幅减少实验数据集规模的可能性。
