Learning a variational network for reconstruction of accelerated MRI data.
Learning a variational network for reconstruction of accelerated MRI data.
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DOI：
10.1002/mrm.26977
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发表时间：
2018-06
影响因子：
3.3
通讯作者：
Knoll F
中科院分区：
文献类型：
--
作者：
Hammernik K;Klatzer T;Kobler E;Recht MP;Sodickson DK;Pock T;Knoll F
通过学习结合了变分模型的数学结构和深度学习的变分网络，可以快速、高质量地重建临床加速的多线圈磁共振数据。将广义压缩感知重构作为变分模型嵌入到展开的梯度下降格式中。该公式的所有参数，包括由过滤器核和激活函数定义的先验模型以及数据项权重，在离线训练过程中被学习。然后，学习到的模型可以在线应用于以前未见过的数据。对于不同加速因子和采样模式的临床膝关节成像方案，使用回溯性和前瞻性采样不足的数据对变分网络方法进行了评估。变分网络重建优于标准重建算法，通过定量误差测量和临床读者对规则采样和加速因子4的研究进行了验证。变分网络重建保留了MR图像的自然外观以及未包括在训练数据集中的病理。由于其高计算性能，即在单个图形卡上重建时间为193ms，并且在网络训练后省去了参数调整，这种新的图像重建方法可以很容易地集成到临床工作流中。
To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning. Generalized compressed sensing reconstruction formulated as a variational model is embedded in an unrolled gradient descent scheme. All parameters of this formulation, including the prior model defined by filter kernels and activation functions as well as the data term weights, are learned during an offline training procedure. The learned model can then be applied online to previously unseen data. The variational network approach is evaluated on a clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data. The variational network reconstructions outperform standard reconstruction algorithms, verified by quantitative error measures and a clinical reader study for regular sampling and acceleration factor 4. Variational network reconstructions preserve the natural appearance of MR images as well as pathologies that were not included in the training data set. Due to its high computational performance, i.e., reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow.
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