用自适应稀疏图提升图像重建可解释性与鲁棒性
Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries
- 通过神经网络生成空间自适应稀疏度图，优化字典正则化
- 在低场MRI中优于其他深度学习方法，尤其对分布外数据更稳健
- 支持推理时更换字典，适合需灵活调整的医学成像场景
当前先进的学习型重建方法常依赖黑箱模块，虽性能优异但可解释性与鲁棒性存疑。本文基于一种新提出的图像重建方法，利用神经网络推断的空间自适应稀疏度图，将数据驱动信息嵌入基于模型的卷积字典正则化。通过改进网络设计与专用训练策略，扩展了方法以实现滤波器置换不变性，并支持推理时更换卷积字典。我们在低场MRI上进行了实验，对比了多种近期基于深度学习的方法，包括活体数据，结果表明使用不同字典能带来明显优势。进一步评估了方法在分布内和分布外数据上的鲁棒性，发现在分布外数据测试中，该方法受数据分布偏移影响较小，归因于其基于模型的重建组件减少了对训练数据的依赖。
原文摘要 · Abstract (English)
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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