使用深度生成SToRM（Gen-SToRM）模型的动态成像
Dynamic imaging using a deep generative SToRM (Gen-SToRM) model
摘要：
我们介绍了一个生成平滑正则化流形（SToRM）模型的动态图像数据的恢复高度欠采样测量。该模型假设数据集中的图像是低维潜向量的非线性映射。我们使用深度卷积神经网络（CNN）来表示非线性变换。生成器的参数以及低维特征向量仅从欠采样测量值联合估计。这种方法不同于传统的CNN方法，传统的CNN方法需要大量的完全采样的训练数据。我们惩罚非线性映射的梯度的范数以约束流形为平滑的，而惩罚潜向量的时间梯度以获得平滑变化的时间序列。所提出的方案引入了卷积网络提供的空间正则化。该方案的主要好处是图像质量的提高和数量级的减少内存需求相比，传统的流形模型。为了最小化算法的计算复杂度，我们引入了一个有效的渐进式训练的时间方法和近似的代价函数。这些方法加快了图像重建的速度，并提供了更好的重建性能。
英文摘要：
We introduce a generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The model assumes that the images in the dataset are non-linear mappings of low-dimensional latent vectors. We use the deep convolutional neural network (CNN) to represent the non-linear transformation. The parameters of the generator as well as the low-dimensional latent vectors are jointly estimated only from the undersampled measurements. This approach is different from traditional CNN approaches that require extensive fully sampled training data. We penalize the norm of the gradients of the non-linear mapping to constrain the manifold to be smooth, while temporal gradients of the latent vectors are penalized to obtain a smoothly varying time-series. The proposed scheme brings in the spatial regularization provided by the convolutional network. The main benefit of the proposed scheme is the improvement in image quality and the orders-of-magnitude reduction in memory demand compared to traditional manifold models. To minimize the computational complexity of the algorithm, we introduce an efficient progressive training-in-time approach and an approximate cost function. These approaches speed up the image reconstructions and offers better reconstruction performance.
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