论文标题
K空间和图像域协作能量基于平行MRI重建的模型
K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstruction
论文作者
论文摘要
减少磁共振（MR）图像采集时间可能会使MR检查更容易获得。包括深度学习模型在内的先前艺术已致力于解决长期MRI成像时间的问题。最近，深层生成模型在算法鲁棒性和使用灵活性方面具有巨大的潜力。然而，没有直接用于K空间测量的现有方案。此外，还值得研究的是，深层生成模型如何在混合域上很好地工作。在这项工作中，通过利用基于深度能量的模型，我们提出了一个K空间和图像域协作生成模型，以全面地估算出降采样测量的MR数据。与最先进的实验比较表明，所提出的混合方法的重建精度的错误较小，并且在不同的加速度因子下更稳定。
Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible. Prior arts including the deep learning models have been devoted to solving the problem of long MRI imaging time. Recently, deep generative models have exhibited great potentials in algorithm robustness and usage flexibility. Nevertheless, none of existing schemes can be learned or employed to the k-space measurement directly. Furthermore, how do the deep generative models work well in hybrid domain is also worth being investigated. In this work, by taking advantage of the deep energy-based models, we propose a k-space and image domain collaborative generative model to comprehensively estimate the MR data from under-sampled measurement. Experimental comparisons with the state-of-the-arts demonstrated that the proposed hybrid method has less error in reconstruction accuracy and is more stable under different acceleration factors
