面向扫描特定MRI重建的无监督自适应隐式神经表示学习
Unsupervised Adaptive Implicit Neural Representation Learning for Scan-Specific MRI Reconstruction
AI总结：
本文提出一种无监督自适应由粗到细框架，利用隐式神经表示和渐进式k空间自监督策略，在不依赖全采样数据且不受欠采样模式限制的情况下提升扫描特定MRI重建质量，并在2D和3D数据上以最高8倍欠采样超越现有方法。
摘要：
在近期关于MRI重建的研究中，相关进展显示出进一步加速MRI采集的巨大潜力。大多数最先进的方法需要大量全采样数据来优化重建模型，这在某些临床环境下不切实际且成本高昂。另一方面，对于无监督的扫描特定重建方法，由于监督不足容易发生过拟合，而对加速倍率和欠采样模式的限制进一步制约了其适用性。为此，我们提出了一种无监督的自适应由粗到细框架，该框架能够提升重建质量，同时不受欠采样中稀疏程度或模式的约束。该框架采用隐式神经表示进行扫描特定MRI重建，学习从多维坐标到其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，逐步细化所采集k空间信号在自监督中的使用方式。该方法能够有效调整来自不同频带中分布不均匀信息的监督信号比例，从而在改善整体重建的同时缓解过拟合问题。在一个包含2D和3D数据的公开数据集上的综合评估表明，在最高8倍欠采样条件下，我们的方法优于当前最先进的扫描特定MRI重建技术。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
