Abstract
Magnetic Resonance Imaging (MRI) is a vital clinical diagnostic tool, yet its widespread application is limited by prolonged scan times. Fast MRI reconstruction techniques effectively reduce acquisition duration by reconstructing high-fidelity MR images from undersampled k-space data. In recent years, deep learning-based methods have demonstrated remarkable progress in this field, with self-supervised and unsupervised learning approaches proving particularly valuable in scenarios where fully sampled data are difficult to obtain. This paper proposes a novel zero-shot self-supervised reconstruction framework named UnrollINR, which enables scan-specific MRI reconstruction without relying on external training data. The method adopts a physics-guided unrolled iterative reconstruction architecture and introduces Implicit Neural Representation (INR) as a regularization prior to effectively constrain the solution space. By combining a deep unrolled structure with the powerful implicit representation capability of INR, the model's interpretability and reconstruction performance are enhanced. Experimental results demonstrate that even at a high acceleration rate of 10, UnrollINR achieves superior reconstruction performance compared to the supervised learning method, validating the superiority of the proposed method.
Abstract (translated)
磁共振成像（MRI）是一种重要的临床诊断工具，但其广泛应用受限于较长的扫描时间。快速MRI重建技术通过从欠采样的k空间数据中重构高保真度的MR图像来有效缩短采集时间。近年来，基于深度学习的方法在这一领域取得了显著进展，特别是在难以获得完全采样数据的情况下，自监督和无监督学习方法特别有价值。本文提出了一种名为UnrollINR的新颖零样本自监督重建框架，该框架可以在无需外部训练数据的情况下实现特定扫描的MRI重建。此方法采用物理引导的展开迭代重建架构，并引入隐式神经表示（Implicit Neural Representation, INR）作为正则化先验，以有效地限制解空间。通过结合深度展开结构和INR强大的隐式表征能力，增强了模型的可解释性和重构性能。实验结果表明，在高达10倍加速率的情况下，UnrollINR仍优于监督学习方法的重建效果，验证了所提出方法的优势。
URL
https://arxiv.org/abs/2510.06611
