A review on deep learning MRI reconstruction without fully sampled k-space.
A review on deep learning MRI reconstruction without fully sampled k-space.
复制标题
无完全采样k空间的深度学习MRI重建综述
DOI：
10.1186/s12880-021-00727-9
复制
发表时间：
2021-12-24
影响因子：
2.7
通讯作者：
Guo D
中科院分区：
文献类型：
--
作者：
Zeng G;Guo Y;Zhan J;Wang Z;Lai Z;Du X;Qu X;Guo D
磁共振成像（MRI）是临床医学中一种有效的辅助诊断手段，但一直存在采集时间长的问题。压缩传感和并行成像是加速MRI重建的两种常见技术。最近，深度学习为MRI提供了一个新的方向，而其中大多数都需要大量的数据对进行训练。然而，在很多情况下，无法获得完全采样的k空间数据，这将严重阻碍监督学习的应用。因此，没有完全采样数据的深度学习是必不可少的。在这篇综述中，我们首先介绍了MRI的正向模型作为一个经典的逆问题，并简要讨论了传统迭代方法与深度学习的联系。接下来，我们将从获取先验信息的角度解释如何在没有完全采样数据的情况下训练重建网络。虽然审查的方法用于MRI重建，他们也可以扩展到其他领域的地面实况是不可用的。此外，我们可以预期，传统方法和深度学习的结合将产生更好的重建结果。
Magnetic resonance imaging (MRI) is an effective auxiliary diagnostic method in clinical medicine, but it has always suffered from the problem of long acquisition time. Compressed sensing and parallel imaging are two common techniques to accelerate MRI reconstruction. Recently, deep learning provides a new direction for MRI, while most of them require a large number of data pairs for training. However, there are many scenarios where fully sampled k-space data cannot be obtained, which will seriously hinder the application of supervised learning. Therefore, deep learning without fully sampled data is indispensable. In this review, we first introduce the forward model of MRI as a classic inverse problem, and briefly discuss the connection of traditional iterative methods to deep learning. Next, we will explain how to train reconstruction network without fully sampled data from the perspective of obtaining prior information. Although the reviewed methods are used for MRI reconstruction, they can also be extended to other areas where ground-truth is not available. Furthermore, we may anticipate that the combination of traditional methods and deep learning will produce better reconstruction results.
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影响因子：
3.3
作者：
Haldar JP;Zhuo J
通讯作者：
Zhuo J
影响因子：
3.3
作者：
Hammernik K;Klatzer T;Kobler E;Recht MP;Sodickson DK;Pock T;Knoll F
通讯作者：
Knoll F
影响因子：
3
作者：
Daubechies, I;Defrise, M;De Mol, C
通讯作者：
De Mol, C
DOI：
10.1109/embc44109.2020.9176241
发表时间：
2020-07
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子：
--
作者：
Hossein Hosseini SA;Yaman B;Moeller S;Akcakaya M
通讯作者：
Akcakaya M
影响因子：
3.3
作者：
Griswold, MA;Jakob, PM;Haase, A
通讯作者：
Haase, A