提出DA-INR模型，提升动态MRI重建质量并大幅缩短优化时间。
Dynamic-Aware Spatio-temporal Representation Learning for Dynamic MRI Reconstruction
- 通过显式建模时间冗余性，捕捉动态MRI的时空连续性。
- 在极端欠采样下仍保持高质量重建，优化时间显著减少。
- 适合需要快速高精度重建的临床动态MRI场景。
动态MRI重建作为逆问题，近年来借助深度学习技术取得显著进展。由于真实标签数据获取困难，无监督学习方法应运而生。其中，隐式神经表示（INR）通过将数据定义为坐标到信号值的连续函数，仅凭不完整测量即可填补缺失信息，有效求解逆问题。然而，现有基于INR的方法存在优化耗时长、需大量超参数调优等缺陷。为此，本文提出动态感知的INR（DA-INR），在图像域中同时建模动态MRI数据的空间与时间连续性，并将时间冗余性显式融入模型结构。实验表明，DA-INR在极端欠采样条件下仍优于其他模型，重建质量更高，且优化时间大幅降低，超参数调优需求极少。
原文摘要 · Abstract (English)
Dynamic MRI reconstruction, one of inverse problems, has seen a surge by the use of deep learning techniques. Especially, the practical difficulty of obtaining ground truth data has led to the emergence of unsupervised learning approaches. A recent promising method among them is implicit neural representation (INR), which defines the data as a continuous function that maps coordinate values to the corresponding signal values. This allows for filling in missing information only with incomplete measurements and solving the inverse problem effectively. Nevertheless, previous works incorporating this method have faced drawbacks such as long optimization time and the need for extensive hyperparameter tuning. To address these issues, we propose Dynamic-Aware INR (DA-INR), an INR-based model for dynamic MRI reconstruction that captures the spatial and temporal continuity of dynamic MRI data in the image domain and explicitly incorporates the temporal redundancy of the data into the model structure. As a result, DA-INR outperforms other models in reconstruction quality even at extreme undersampling ratios while significantly reducing optimization time and requiring minimal hyperparameter tuning.
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