Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI With Limited Training Data
Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI With Limited Training Data
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DOI：
10.1109/tmi.2019.2930318
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发表时间：
2020-03-01
影响因子：
10.6
通讯作者：
Kolbitsch, Christoph
中科院分区：
文献类型：
--
作者：
Kofler, Andreas;Dewey, Marc;Kolbitsch, Christoph
在这项工作中，我们减少采样不足的伪影在二维（2D）黄金角放射状电影心脏MRI应用修改后的版本的U-网。该网络是训练的二维时空切片，这是以前从图像序列中提取。我们将我们的方法与两种基于2D和3D深度学习的后处理方法，三种迭代重建方法以及两种最近提出的基于2D和3D级联网络的动态心脏MRI方法进行了比较。我们的方法优于2D空间训练的U-网和2D时空U-网。与3D时空U网相比，我们的方法提供了相当的结果，但需要更短的训练时间和更少的训练数据。与基于压缩感知的方法kt-FOTORS和全变分正则化重建方法相比，我们的方法在所有报告的度量方面提高了图像质量。此外，当与基于具有字典学习和总变分的自适应正则化的迭代重建方法相比时，以及当与基于级联网络的方法相比时，它实现了有竞争力的结果，同时仅需要一小部分计算和训练时间。持久的同源性分析表明，时空域的数据流形具有较低的复杂性比空间域的一个，因此，学习的投影映射是方便的。即使在没有数据增强的情况下只对一个单一主题进行训练，我们的方法也会产生类似于在大型训练数据集上获得的结果。这使得该方法特别适合于在有限的训练数据上训练网络。最后，与空间2D U网相比，我们提出的方法在图像空间中的图像旋转方面表现出自然的鲁棒性，并且几乎实现了旋转等方差，其中既不需要数据增强也不需要特定的网络设计。
In this work we reduce undersampling artefacts in two-dimensional (2D) golden-angle radial cine cardiac MRI by applying a modified version of the U-net. The network is trained on 2D spatio-temporal slices which are previously extracted from the image sequences. We compare our approach to two 2D and a 3D deep learning-based post processing methods, three iterative reconstruction methods and two recently proposed methods for dynamic cardiac MRI based on 2D and 3D cascaded networks. Our method outperforms the 2D spatially trained U-net and the 2D spatio-temporal U-net. Compared to the 3D spatio-temporal U-net, our method delivers comparable results, but requiring shorter training times and less training data. Compared to the compressed sensing-based methods kt-FOCUSS and a total variation regularized reconstruction approach, our method improves image quality with respect to all reported metrics. Further, it achieves competitive results when compared to the iterative reconstruction method based on adaptive regularization with dictionary learning and total variation and when compared to the methods based on cascaded networks, while only requiring a small fraction of the computational and training time. A persistent homology analysis demonstrates that the data manifold of the spatio-temporal domain has a lower complexity than the one of the spatial domain and therefore, the learning of a projection-like mapping is facilitated. Even when trained on only one single subject without data-augmentation, our approach yields results which are similar to the ones obtained on a large training dataset. This makes the method particularly suitable for training a network on limited training data. Finally, in contrast to the spatial 2D U-net, our proposed method is shown to be naturally robust with respect to image rotation in image space and almost achieves rotation-equivariance where neither data-augmentation nor a particular network design are required.