Graduate School, Istanbul Technical University, Istanbul, Turkey.
Electronics and Communication Engineering Department, Istanbul Technical University, Istanbul, Turkey.
Comput Biol Med. 2021 Dec;139:105010. doi: 10.1016/j.compbiomed.2021.105010. Epub 2021 Nov 6.
MR image reconstruction techniques based on deep learning have shown their capacity for reducing MRI acquisition time and performance improvement compared to analytical methods. Despite the many challenges in training these rather large networks, novel methodologies have enhanced the capability for having clinical-grade MR image reconstruction in real-time. In recent literature, novel developments have facilitated the utilization of deep networks in various image processing inverse problems. In particular, it has been reported multiple times that the performance of deep networks can be improved by using short connections between layers. In this study, we introduce a novel MRI reconstruction method that utilizes such short connections. The dense connections are used inside densely connected residual blocks. Inside these blocks, the feature maps are concatenated to the subsequent layers. In this way, the extracted information is propagated until the last stage of the block. We have evaluated this densely connected residual block's efficiency in MRI reconstruction settings, by augmenting different types of effective deep network models with these blocks in novel structures. The quantitative and qualitative results indicate that this original introduction of the densely connected blocks to the MR image reconstruction problem improves the reconstruction performance significantly.
基于深度学习的磁共振图像重建技术在减少磁共振成像采集时间和提高性能方面表现出了优于分析方法的能力。尽管在训练这些相当大的网络方面存在许多挑战，但新的方法学提高了实时进行临床级磁共振图像重建的能力。在最近的文献中，新的发展促进了深度网络在各种图像处理逆问题中的应用。特别是，已经多次报道，通过在层之间使用短连接，可以提高深度网络的性能。在这项研究中，我们介绍了一种新的磁共振成像重建方法，该方法利用了这种短连接。密集连接用于密集连接的残差块内部。在这些块中，特征图被连接到后续层。通过这种方式，提取的信息一直传播到块的最后一个阶段。我们通过在不同类型的有效深度网络模型中引入这些块来评估这种密集连接残差块在磁共振成像重建中的效率。定量和定性结果表明，这种将密集连接块引入磁共振图像重建问题的方法显著提高了重建性能。
