用可解释的算法框架实现自监督磁共振成像重建与去噪
Learned Primal Dual Splitting for Self-Supervised Noise-Adaptive MRI Reconstruction
- 直接学习经典原始对偶分裂算法，解耦观测模型与信号先验
- 自监督下性能超越现有方法，且能泛化到不同噪声水平
- 适合关注模型可解释性与实际医学图像处理的应用场景
磁共振成像（MRI）重建主要依赖深度神经网络（DNN），但许多先进架构采用黑箱结构，阻碍了可解释性与改进。本文提出一种可解释的DNN架构——LPDSNet，通过直接参数化并学习经典的原始对偶分裂算法，实现自监督下的MRI重建与去噪。该方法使观测模型与信号先验得以解耦。实验表明，缺乏此解耦特性的可解释架构在自监督学习中表现失败。我们报告了当前最先进的自监督联合重建与去噪性能，并发现其具备新颖的噪声水平泛化能力，而黑箱网络则无法实现此类泛化。
原文摘要 · Abstract (English)
Magnetic resonance imaging (MRI) reconstruction has largely been dominated by deep neural networks (DNN); however, many state-of-the-art architectures use black-box structures, which hinder interpretability and improvement. Here, we propose an interpretable DNN architecture for self-supervised MRI reconstruction and denoising by directly parameterizing and learning the classical primal-dual splitting, dubbed LPDSNet. This splitting algorithm allows us to decouple the observation model from the signal prior. Experimentally, we show other interpretable architectures without this decoupling property exhibit failure in the self-supervised learning regime. We report state-of-the-art self-supervised joint MRI reconstruction and denoising performance and novel noise-level generalization capabilities, where in contrast black-box networks fail to generalize.
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