论文标题
水/脂肪分离的深神经网络（DNN）：监督培训，无监督培训，没有培训
Deep Neural Network (DNN) for Water/Fat Separation: Supervised Training, Unsupervised Training, and No Training
论文作者
论文摘要
目的：使用深层神经网络（DNN）来解决水/脂肪分离的优化问题，并比较受监督和无监督的培训。 方法：用于求解脂肪/水分离的当前T2*理想算法取决于初始化。最近，已经提出了深层神经网络（DNN）来解决脂肪/水分离，而无需适当的初始化。但是，这种方法需要使用参考脂肪/水分离图像对DNN（STD）进行监督培训。在这里，我们提出了两种新型的DNN水/脂肪分离方法1）使用物理前进问题作为训练过程中的成本功能，对DNN（UTD）进行无监督的训练，以及2）使用物理成本和反向传播对DNN（NTD）进行无培训，以直接重建一个单个数据集。将STD，UTD和NTD方法与参考T2* - 理想的方法进行了比较。 结果：所有DNN方法都会产生一致的水/脂肪分离结果，在适当初始化下与T2* - 理想吻合。 结论：可以使用无监督的深神经网络解决水/脂肪分离问题。
Purpose: To use a deep neural network (DNN) for solving the optimization problem of water/fat separation and to compare supervised and unsupervised training. Methods: The current T2*-IDEAL algorithm for solving fat/water separation is dependent on initialization. Recently, deep neural networks (DNN) have been proposed to solve fat/water separation without the need for suitable initialization. However, this approach requires supervised training of DNN (STD) using the reference fat/water separation images. Here we propose two novel DNN water/fat separation methods 1) unsupervised training of DNN (UTD) using the physical forward problem as the cost function during training, and 2) no-training of DNN (NTD) using physical cost and backpropagation to directly reconstruct a single dataset. The STD, UTD and NTD methods were compared with the reference T2*-IDEAL. Results: All DNN methods generated consistent water/fat separation results that agreed well with T2*-IDEAL under proper initialization. Conclusion: The water/fat separation problem can be solved using unsupervised deep neural networks.
