Deep Parallel MRI Reconstruction Network Without Coil Sensitivities
Deep Parallel MRI Reconstruction Network Without Coil Sensitivities
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DOI：
10.1007/978-3-030-61598-7_2
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
Wanyu Bian;Yunmei Chen;X. Ye
中科院分区：
文献类型：
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作者：
Wanyu Bian;Yunmei Chen;X. Ye
我们提出了一种新的深度神经网络架构，通过映射鲁棒的近端梯度方案，用于并行MRI（pMRI）中的快速图像重建，并使用从数据中训练的正则化函数。所提出的网络学习自适应联合收割机将来自不完整pMRI数据的多线圈图像组合成具有均匀对比度的单个图像，然后将其传递到非线性编码器以有效地提取图像的稀疏特征。与大多数现有的深度图像重建网络不同，我们的网络不需要敏感度图的知识，这可能很难准确估计，并且一直是现实世界pMRI应用中图像重建的主要瓶颈。实验结果表明，我们的方法在各种pMRI成像数据集上的良好性能。
We propose a novel deep neural network architecture by mapping the robust proximal gradient scheme for fast image reconstruction in parallel MRI (pMRI) with regularization function trained from data. The proposed network learns to adaptively combine the multi-coil images from incomplete pMRI data into a single image with homogeneous contrast, which is then passed to a nonlinear encoder to efficiently extract sparse features of the image. Unlike most of existing deep image reconstruction networks, our network does not require knowledge of sensitivity maps, which can be difficult to estimate accurately, and have been a major bottleneck of image reconstruction in real-world pMRI applications. The experimental results demonstrate the promising performance of our method on a variety of pMRI imaging data sets.