In recent studies on MRI reconstruction, advances have shown significant promise for further accelerating the MRI acquisition. Most state-of-the-art methods require a large amount of fully-sampled data to optimise reconstruction models, which is impractical and expensive under certain clinical settings. On the other hand, for unsupervised scan-specific reconstruction methods, overfitting is likely to happen due to insufficient supervision, while restrictions on acceleration rates and under-sampling patterns further limit their applicability. To this end, we propose an unsupervised, adaptive coarse-to-fine framework that enhances reconstruction quality without being constrained by the sparsity levels or patterns in under-sampling. The framework employs an implicit neural representation for scan-specific MRI reconstruction, learning a mapping from multi-dimensional coordinates to their corresponding signal intensities. Moreover, we integrate a novel learning strategy that progressively refines the use of acquired k-space signals for self-supervision. This approach effectively adjusts the proportion of supervising signals from unevenly distributed information across different frequency bands, thus mitigating the issue of overfitting while improving the overall reconstruction. Comprehensive evaluation on a public dataset, including both 2D and 3D data, has shown that our method outperforms current state-of-the-art scan-specific MRI reconstruction techniques, for up to 8-fold under-sampling.

翻译：在MRI重建的最新研究中，相关进展在进一步加速MRI采集方面展现出巨大潜力。多数最先进方法需要大量全采样数据来优化重建模型，这在某些临床条件下既不切实际且成本高昂。另一方面，对于无监督的扫描特异性重建方法，由于监督不足容易导致过拟合，同时加速率和欠采样模式的限制进一步制约了其适用性。为此，我们提出一种无监督自适应由粗到细框架，该框架在不受欠采样稀疏度或模式约束的前提下提升重建质量。该框架采用隐式神经表示进行扫描特异性MRI重建，学习从多维坐标到对应信号强度的映射。此外，我们集成了一种新颖的学习策略，通过逐步优化对已采集k空间信号的利用进行自监督。该方法能有效调整来自不同频带非均匀分布信息的监督信号比例，从而在缓解过拟合问题的同时提升整体重建效果。在包含二维和三维数据的公开数据集上的综合评估表明，我们的方法在高达8倍欠采样条件下均优于当前最先进的扫描特异性MRI重建技术。