Tu Zongjiang, Jiang Chen, Guan Yu, Liu Jijun, Liu Qiegen
Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China.
Department of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China.
Magn Reson Imaging. 2023 Jun;99:110-122. doi: 10.1016/j.mri.2023.02.004. Epub 2023 Feb 15.
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 from 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. Equipped with parallel and sequential orders, experimental comparisons with the state-of-the-arts demonstrated that they involve less error in reconstruction accuracy and are more stable under different acceleration factors.
缩短磁共振（MR）图像采集时间可能会使MR检查更容易进行。包括深度学习模型在内的现有技术一直致力于解决MRI成像时间长的问题。最近，深度生成模型在算法鲁棒性和使用灵活性方面展现出了巨大潜力。然而，现有的方案都无法直接从k空间测量中学习或应用于k空间测量。此外，深度生成模型在混合域中如何良好工作也值得研究。在这项工作中，我们利用基于深度能量的模型，提出了一种k空间和图像域协作生成模型，以从欠采样测量中全面估计MR数据。该模型具有并行和顺序顺序，与现有技术的实验比较表明，它们在重建精度方面误差更小，在不同加速因子下更稳定。
