Tewari Sumit, Yousefi Sahar, Webb Andrew
C.J. Gorter Center for High Field MRI, Radiology, Leiden University Medical Center, Leiden, The Netherlands.
Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands.
Inverse Probl. 2022 Jan 26;38(3):035003. doi: 10.1088/1361-6420/ac492a.
We present a combination of a CNN-based encoder with an analytical forward map for solving inverse problems. We call it an encoder-analytic (EA) hybrid model. It does not require a dedicated training dataset and can train itself from the connected forward map in a direct learning fashion. A separate regularization term is not required either, since the forward map also acts as a regularizer. As it is not a generalization model it does not suffer from overfitting. We further show that the model can be customized to either find a specific target solution or one that follows a given heuristic. As an example, we apply this approach to the design of a multi-element surface magnet for low-field magnetic resonance imaging (MRI). We further show that the EA model can outperform the benchmark genetic algorithm model currently used for magnet design in MRI, obtaining almost 10 times better results.
我们提出了一种基于卷积神经网络（CNN）的编码器与解析正向映射相结合的方法来解决逆问题。我们将其称为编码器-解析（EA）混合模型。它不需要专门的训练数据集，并且可以通过直接学习方式从连接的正向映射中进行自我训练。也不需要单独的正则化项，因为正向映射也起到正则化的作用。由于它不是一个泛化模型，所以不会遭受过拟合问题。我们进一步表明，该模型可以定制为找到特定的目标解或遵循给定启发式的解。例如，我们将这种方法应用于低场磁共振成像（MRI）的多元素表面磁体设计。我们还表明，EA模型的性能优于目前用于MRI磁体设计的基准遗传算法模型，结果几乎好出10倍。
