Jiang Ming-Feng, Chen Yun-Jiang, Ruan Dong-Sheng, Yuan Zi-Han, Zhang Ju-Cheng, Xia Ling
School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, Zhejiang, China.
The Second Affiliated Hospital, School of Medicine Zhejiang University, Hangzhou, Zhejiang, China.
Med Phys. 2025 Jan;52(1):388-399. doi: 10.1002/mp.17501. Epub 2024 Nov 28.
Recent advances in deep learning have sparked new research interests in dynamic magnetic resonance imaging (MRI) reconstruction. However, existing deep learning-based approaches suffer from insufficient reconstruction efficiency and accuracy due to the lack of time correlation modeling during the reconstruction procedure.
Inappropriate tensor processing steps and deep learning models may lead to not only a lack of modeling in the time dimension but also an increase in the overall size of the network. Therefore, this study aims to find suitable tensor processing methods and deep learning models to achieve better reconstruction results and a smaller network size.
We propose a novel unrolling network method that enhances the reconstruction quality and reduces the parameter redundancy by introducing time correlation modeling into MRI reconstruction with low-rank core matrix and convolutional long short-term memory (ConvLSTM) unit.
We conduct extensive experiments on AMRG Cardiac MRI dataset to evaluate our proposed approach. The results demonstrate that compared to other state-of-the-art approaches, our approach achieves higher peak signal-to-noise ratios and structural similarity indices at different accelerator factors with significantly fewer parameters.
The improved reconstruction performance demonstrates that our proposed time correlation modeling is simple and effective for accelerating MRI reconstruction. We hope our approach can serve as a reference for future research in dynamic MRI reconstruction.
深度学习的最新进展引发了对动态磁共振成像（MRI）重建的新研究兴趣。然而，由于在重建过程中缺乏时间相关性建模，现有的基于深度学习的方法存在重建效率和准确性不足的问题。
不适当的张量处理步骤和深度学习模型不仅可能导致在时间维度上缺乏建模，还可能导致网络整体规模增加。因此，本研究旨在找到合适的张量处理方法和深度学习模型，以获得更好的重建结果和更小的网络规模。
我们提出了一种新颖的展开网络方法，通过将时间相关性建模引入具有低秩核心矩阵和卷积长短期记忆（ConvLSTM）单元的MRI重建中，提高重建质量并减少参数冗余。
我们在AMRG心脏MRI数据集上进行了广泛的实验，以评估我们提出的方法。结果表明，与其他现有先进方法相比，我们的方法在不同加速因子下实现了更高的峰值信噪比和结构相似性指数，且参数显著更少。
改进的重建性能表明，我们提出的时间相关性建模对于加速MRI重建简单有效。我们希望我们的方法可以为动态MRI重建的未来研究提供参考。
