IEEE Trans Med Imaging. 2021 Dec;40(12):3265-3278. doi: 10.1109/TMI.2021.3081677. Epub 2021 Nov 30.
Deep learning, particularly the generative model, has demonstrated tremendous potential to significantly speed up image reconstruction with reduced measurements recently. Rather than the existing generative models that often optimize the density priors, in this work, by taking advantage of the denoising score matching, homotopic gradients of generative density priors (HGGDP) are exploited for magnetic resonance imaging (MRI) reconstruction. More precisely, to tackle the low-dimensional manifold and low data density region issues in generative density prior, we estimate the target gradients in higher-dimensional space. We train a more powerful noise conditional score network by forming high-dimensional tensor as the network input at the training phase. More artificial noise is also injected in the embedding space. At the reconstruction stage, a homotopy method is employed to pursue the density prior, such as to boost the reconstruction performance. Experiment results implied the remarkable performance of HGGDP in terms of high reconstruction accuracy. Only 10% of the k-space data can still generate image of high quality as effectively as standard MRI reconstructions with the fully sampled data.
深度学习，特别是生成模型，最近在减少测量的情况下显著加快图像重建方面表现出巨大潜力。在这项工作中，与现有的通常优化密度先验的生成模型不同，我们利用去噪得分匹配，针对磁共振成像 (MRI) 重建，利用生成密度先验的同伦梯度 (HGGDP)。更准确地说，为了解决生成密度先验中的低维流形和低数据密度区域问题，我们在高维空间中估计目标梯度。在训练阶段，我们通过将高维张量作为网络输入来训练更强大的噪声条件得分网络。在嵌入空间中也注入了更多的人工噪声。在重建阶段，采用同伦方法来追求密度先验，以提高重建性能。实验结果表明，HGGDP 在高重建精度方面具有显著的性能。仅使用 10%的 k 空间数据，仍然可以像使用完全采样数据的标准 MRI 重建一样有效地生成高质量的图像。
