论文标题
抗压MR指纹重建具有神经近端梯度迭代的重建
Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations
论文作者
论文摘要
相对于物理前向模型的预测的一致性对于可靠地解决反问题是关键的。这种一致性主要是在为磁共振指纹（MRF）问题提出的当前端到端深度学习方法中无法控制的。为了解决这个问题，我们提出了Proxnet，这是一种学到的近端梯度下降框架，将正向采集和Bloch动态模型直接纳入了经常性学习机制。该网络采用了一个紧凑的神经近端模型，用于去脱氧和定量推断，可以灵活地在稀缺的MRF培训数据集中训练。我们的数值实验表明，Proxnet可以达到较高的定量推断准确性，较小的存储需求以及与最近深度学习的MRF基线的可比运行时，同时比字典匹配方案快得多。代码已在https://github.com/edongdongchen/pgd-net上发布。
Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems. This consistency is mostly un-controlled in the current end-to-end deep learning methodologies proposed for the Magnetic Resonance Fingerprinting (MRF) problem. To address this, we propose ProxNet, a learned proximal gradient descent framework that directly incorporates the forward acquisition and Bloch dynamic models within a recurrent learning mechanism. The ProxNet adopts a compact neural proximal model for de-aliasing and quantitative inference, that can be flexibly trained on scarce MRF training datasets. Our numerical experiments show that the ProxNet can achieve a superior quantitative inference accuracy, much smaller storage requirement, and a comparable runtime to the recent deep learning MRF baselines, while being much faster than the dictionary matching schemes. Code has been released at https://github.com/edongdongchen/PGD-Net.
