There remains an important need for the development of image reconstruction methods that can produce diagnostically useful images from undersampled measurements. In magnetic resonance imaging (MRI), for example, such methods can facilitate reductions in data-acquisition times. Deep learning-based methods hold potential for learning object priors or constraints that can serve to mitigate the effects of data-incompleteness on image reconstruction. One line of emerging research involves formulating an optimization-based reconstruction method in the latent space of a generative deep neural network. However, when generative adversarial networks (GANs) are employed, such methods can result in image reconstruction errors if the sought-after solution does not reside within the range of the GAN. To circumvent this problem, in this work, a framework for reconstructing images from incomplete measurements is proposed that is formulated in the latent space of invertible neural network-based generative models. A novel regularization strategy is introduced that takes advantage of the multiscale architecture of certain invertible neural networks, which can result in improved reconstruction performance over classical methods in terms of traditional metrics. The proposed method is investigated for reconstructing images from undersampled MRI data. The method is shown to achieve comparable performance to a state-of-the-art generative model-based reconstruction method while benefiting from a deterministic reconstruction procedure and easier control over regularization parameters.

中文翻译：

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用于生成模型约束图像重建的可压缩潜在空间可逆网络

仍然非常需要开发能够从欠采样测量中产生诊断有用的图像的图像重建方法。例如，在磁共振成像 (MRI) 中，此类方法有助于减少数据采集时间。基于深度学习的方法具有学习对象先验或约束的潜力，可用于减轻数据不完整对图像重建的影响。一项新兴研究涉及在生成深度神经网络的潜在空间中制定基于优化的重建方法。然而，当使用生成对抗网络 (GAN) 时，如果所寻求的解决方案不在 GAN 的范围内，这些方法可能会导致图像重建错误。为了规避这个问题，在这项工作中，提出了一种从不完整测量中重建图像的框架，该框架在基于可逆神经网络的生成模型的潜在空间中制定。引入了一种新的正则化策略，该策略利用了某些可逆神经网络的多尺度架构，在传统度量方面，这可以提高经典方法的重建性能。所提出的方法被研究用于从欠采样的 MRI 数据中重建图像。该方法被证明可以实现与最先进的基于生成模型的重建方法相当的性能，同时受益于确定性重建过程和更容易控制正则化参数。