论文标题
MR重建的广义深度学习近端梯度下降
Generalized Deep Learning-based Proximal Gradient Descent for MR Reconstruction
论文作者
论文摘要
物理前向模型的数据一致性在反问题中至关重要，尤其是在MR成像重建中。标准方法是将迭代算法展开到具有嵌入式模型的神经网络中。远期模型总是在临床实践中发生变化，因此学习组件与正向模型的纠缠使重建很难概括。提出了基于深度学习的近端梯度下降，并将网络用作独立于正向模型的正规化项，这使其更适合不同的MR采集设置。该一次性预训练的正则化适用于不同的MR采集设置，并将其与传统的L1正则化进行了比较，显示峰值信噪比的改善〜3 dB。我们还证明了所提出的方法在选择不同的不足采样模式方面的灵活性。
The data consistency for the physical forward model is crucial in inverse problems, especially in MR imaging reconstruction. The standard way is to unroll an iterative algorithm into a neural network with a forward model embedded. The forward model always changes in clinical practice, so the learning component's entanglement with the forward model makes the reconstruction hard to generalize. The deep learning-based proximal gradient descent was proposed and use a network as regularization term that is independent of the forward model, which makes it more generalizable for different MR acquisition settings. This one-time pre-trained regularization is applied to different MR acquisition settings and was compared to conventional L1 regularization showing ~3 dB improvement in the peak signal-to-noise ratio. We also demonstrated the flexibility of the proposed method in choosing different undersampling patterns.
