IEEE Trans Pattern Anal Mach Intell. 2023 Aug;45(8):9627-9638. doi: 10.1109/TPAMI.2023.3240565. Epub 2023 Jun 30.
Neural networks (NNs) have been widely applied in tomographic imaging through data-driven training and image processing. One of the main challenges in using NNs in real medical imaging is the requirement of massive amounts of training data - which are not always available in clinical practice. In this article, we demonstrate that, on the contrary, one can directly execute image reconstruction using NNs without training data. The key idea is to bring in the recently introduced deep image prior (DIP) and merge it with electrical impedance tomography (EIT) reconstruction. DIP provides a novel approach to the regularization of EIT reconstruction problems by compelling the recovered image to be synthesized from a given NN architecture. Then, by relying on the NN's built-in back-propagation and the finite element solver, the conductivity distribution is optimized. Quantitative results based on simulation and experimental data show that the proposed method is an effective unsupervised approach capable of outperforming state-of-the-art alternatives.
神经网络 (NNs) 通过数据驱动的训练和图像处理已经在层析成像中得到了广泛应用。在实际医学成像中使用神经网络的主要挑战之一是需要大量的训练数据——而这些数据在临床实践中并不总是可用的。在本文中，我们证明，相反，人们可以直接使用神经网络执行图像重建，而无需使用训练数据。关键思想是引入最近提出的深度图像先验 (DIP) 并将其与电阻抗断层成像 (EIT) 重建相结合。DIP 通过迫使恢复的图像从给定的 NN 架构中合成，为 EIT 重建问题的正则化提供了一种新方法。然后，通过依赖 NN 的内置反向传播和有限元求解器，对电导率分布进行优化。基于模拟和实验数据的定量结果表明，所提出的方法是一种有效的无监督方法，能够优于最先进的替代方法。
