学习任意卷积字典的时空自适应稀疏性水平图
Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries
AI总结：
本文提出了一种学习方法，通过改进的网络设计和专门的训练策略，扩展了基于神经网络推断的时空自适应稀疏性水平图的图像重建方法，实现了滤波器排列不变性，并在低场MRI中展示了使用不同字典的优势。
摘要：
最先进的学习重建方法通常依赖于黑盒模块，尽管性能强大，但对其可解释性和鲁棒性提出了质疑。本文基于最近提出的一种图像重建方法，通过将数据驱动的信息嵌入到基于模型的卷积字典正则化中，利用神经网络推断的时空自适应稀疏性水平图。通过改进的网络设计和专门的训练策略，我们扩展了该方法，以实现滤波器排列不变性以及在推理时更改卷积字典的可能性。我们将该方法应用于低场MRI，并与其他几种最近的深度学习方法进行了比较，包括体内数据，展示了使用不同字典的优势。我们进一步评估了该方法在测试体内和体外数据时的鲁棒性。当测试体外数据时，所提出的方法比其他学习方法受到的数据分布偏移影响更小，这归因于其基于模型的重建组件对训练数据的依赖性较低。
英文摘要：
State-of-the-art learned reconstruction methods often rely on black-box modules that, despite their strong performance, raise questions about their interpretability and robustness. Here, we build on a recently proposed image reconstruction method, which is based on embedding data-driven information into a model-based convolutional dictionary regularization via neural network-inferred spatially adaptive sparsity level maps. By means of improved network design and dedicated training strategies, we extend the method to achieve filter-permutation invariance as well as the possibility to change the convolutional dictionary at inference time. We apply our method to low-field MRI and compare it to several other recent deep learning-based methods, also on in vivo data, where the benefit of using a different dictionary is demonstrated. We further assess the method's robustness when tested on in- and out-of-distribution data. When tested on the latter, the proposed method suffers less from the data distribution shift compared to the other learned methods, which we attribute to its reduced reliance on training data due to its underlying model-based reconstruction component.
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