论文标题
在不完全采样的训练数据的情况下，基于神经网络的重建MRI中的重建
Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data
论文作者
论文摘要
压缩传感MRI（CS-MRI）在重建未采样的MR图像方面表现出了希望，从而提供了减少扫描时间的潜力。经典技术使用昂贵的迭代优化程序最大程度地减少了正规化最小二乘成本功能。最近，已经开发了深度学习模型，通过在神经网络中展开迭代，以模拟经典技术的迭代性质。在表现出卓越的性能的同时，这些方法需要大量的地面图像，并且已证明对看不见的数据是不可理的。在本文中，我们通过在经典优化方案中采用损失功能，以无监督的方式探索一种新型策略，以无监督的方式培训展开的重建网络。我们证明，与经典优化求解器相比，该策略的损失较低，并且在计算上便宜，同时与监督模型相比也表现出优异的鲁棒性。代码可在https://github.com/alanqrwang/hqsnet上找到。
Compressed Sensing MRI (CS-MRI) has shown promise in reconstructing under-sampled MR images, offering the potential to reduce scan times. Classical techniques minimize a regularized least-squares cost function using an expensive iterative optimization procedure. Recently, deep learning models have been developed that model the iterative nature of classical techniques by unrolling iterations in a neural network. While exhibiting superior performance, these methods require large quantities of ground-truth images and have shown to be non-robust to unseen data. In this paper, we explore a novel strategy to train an unrolled reconstruction network in an unsupervised fashion by adopting a loss function widely-used in classical optimization schemes. We demonstrate that this strategy achieves lower loss and is computationally cheap compared to classical optimization solvers while also exhibiting superior robustness compared to supervised models. Code is available at https://github.com/alanqrwang/HQSNet.
