Abstract
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.
Abstract (translated)
在最近的研究中，MRI重建取得了显著的进展，进一步加速了MRI采集。大多数最先进的方法需要大量完全采样数据来优化重建模型，这在某些临床设置下是不切实际的，而且代价昂贵。另一方面，对于无监督的扫描特定重建方法，由于监督不足，过拟合很可能发生，而限制加速率和欠采样模式进一步限制了它们的适用性。因此，我们提出了一个无监督、自适应的粗到细框架，该框架不会受到欠采样级数或模式的限制。该框架使用了一个隐式神经表示来进行扫描特定的MRI重建，从多维坐标到相应的信号强度学习映射。此外，我们还集成了一种新的学习策略，该策略逐渐优化了已获得的k空间信号的自监督使用。这种方法有效地调整了不同频率带之间信息分布不均匀的情况下监督信号的比例，从而缓解了过拟合的问题，同时提高了整体重建。在公开数据集上进行全面的评估，包括2D和3D数据，已经证明了我们的方法在失真量可以达到8倍的情况下优于当前的扫描特定MRI重建技术。
URL
https://arxiv.org/abs/2312.00677
