arXiv 2501.108512025-01-18eess.IVcs.CV

探索自监督快速MRI重建中的孪生网络

Exploring Siamese Networks in Self-Supervised Fast MRI Reconstruction

Stanford University School of Medicine（斯坦福大学医学院）；Xiamen University（厦门大学）；School of Electronic Science and Engineering, Xiamen University（厦门大学电子科学与工程学院）；School of Informatics, Xiamen University（厦门大学信息学院）；Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）

Liyan Sun, Shaocong Yu, Chi Zhang, Xinghao Ding

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AI总结：

本文提出SiamRecon，利用孪生网络进行自监督MRI重建，模拟期望最大化算法，避免平凡解，在单线圈脑和多线圈膝MRI上达到最先进精度。

摘要：

使用深度神经网络从未完全采样的k空间数据重建MR图像，而无需使用完全采样的训练参考，这在实践中具有重要价值，这是一个自监督回归问题，需要有效的先验知识和监督。孪生架构的动机源于“不变性”的定义，并在无监督视觉表示学习中显示出有前景的结果。构建同源变换图像和避免平凡解是基于孪生的自监督模型的两个主要挑战。在本工作中，我们探索了以自监督训练方式（称为SiamRecon）进行MRI重建的孪生架构。我们展示了所提出的方法模拟了期望最大化算法。交替优化提供了有效的监督信号并避免了崩溃。所提出的SiamRecon在单线圈脑MRI和多线圈膝MRI的自监督学习领域达到了最先进的重建精度。

英文摘要：

Reconstructing MR images using deep neural networks from undersampled k-space data without using fully sampled training references offers significant value in practice, which is a self-supervised regression problem calling for effective prior knowledge and supervision. The Siamese architectures are motivated by the definition "invariance" and shows promising results in unsupervised visual representative learning. Building homologous transformed images and avoiding trivial solutions are two major challenges in Siamese-based self-supervised model. In this work, we explore Siamese architecture for MRI reconstruction in a self-supervised training fashion called SiamRecon. We show the proposed approach mimics an expectation maximization algorithm. The alternative optimization provide effective supervision signal and avoid collapse. The proposed SiamRecon achieves the state-of-the-art reconstruction accuracy in the field of self-supervised learning on both single-coil brain MRI and multi-coil knee MRI.

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