论文标题
深层平行的MRI重建网络无线圈灵敏度
Deep Parallel MRI Reconstruction Network Without Coil Sensitivities
论文作者
论文摘要
我们通过映射并行MRI（PMRI）中快速图像重建的稳健近端梯度方案，提出了一种新型的深神经网络结构，并用数据训练了正则化功能。提出的网络学会了将多型映像从不完整的PMRI数据组合到具有均匀对比度的单个图像中，然后将其传递给非线性编码器，以有效地提取图像的稀疏特征。与大多数现有的深层图像重建网络不同，我们的网络不需要了解灵敏度图的知识，这可能很难准确估算，并且是现实世界中PMRI应用程序中图像重建的主要瓶颈。实验结果证明了我们方法在各种PMRI成像数据集上的有希望的性能。
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.
