Hong Guan Qiu, Wei Yuan Tao, Morley William A W, Wan Matthew, Mertens Alexander J, Su Yang, Cheng Hai-Ling Margaret
The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Canada; Institute of Biomedical Engineering, University of Toronto, Canada; Ted Rogers Centre for Heart Research, Translational Biology & Engineering Program, Toronto, Canada.
The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Canada; Ted Rogers Centre for Heart Research, Translational Biology & Engineering Program, Toronto, Canada.
Comput Med Imaging Graph. 2023 Jun;106:102206. doi: 10.1016/j.compmedimag.2023.102206. Epub 2023 Feb 23.
Acceleration in MRI has garnered much attention from the deep-learning community in recent years, particularly for imaging large anatomical volumes such as the abdomen or moving targets such as the heart. A variety of deep learning approaches have been investigated, with most existing works using convolutional neural network (CNN)-based architectures as the reconstruction backbone, paired with fixed, rather than learned, k-space undersampling patterns. In both image domain and k-space, CNN-based architectures may not be optimal for reconstruction due to its limited ability to capture long-range dependencies. Furthermore, fixed undersampling patterns, despite ease of implementation, may not lead to optimal reconstruction. Lastly, few deep learning models to date have leveraged temporal correlation across dynamic MRI data to improve reconstruction. To address these gaps, we present a dual-domain (image and k-space), transformer-based reconstruction network, paired with learning-based undersampling that accepts temporally correlated sequences of MRI images for dynamic reconstruction. We call our model DuDReTLU-net. We train the network end-to-end against fully sampled ground truth dataset. Human cardiac CINE images undersampled at different factors (5-100) were tested. Reconstructed images were assessed both visually and quantitatively via the structural similarity index, mean squared error, and peak signal-to-noise. Experimental results show superior performance of DuDReTLU-net over state-of-the-art methods (LOUPE, k-t SLR, BM3D-MRI) in accelerated MRI reconstruction; ablation studies show that transformer-based reconstruction outperformed CNN-based reconstruction in both image domain and k-space; dual-domain reconstruction architectures outperformed single-domain reconstruction architectures regardless of reconstruction backbone (CNN or transformer); and dynamic sequence input leads to more accurate reconstructions than single frame input. We expect our results to encourage further research in the use of dual-domain architectures, transformer-based architectures, and learning-based undersampling, in the setting of accelerated MRI reconstruction. The code for this project is made freely available at https://github.com/william2343/dual-domain-mri-recon-nets (Hong et al., 2022).
近年来，磁共振成像（MRI）中的加速技术引起了深度学习社区的广泛关注，特别是对于腹部等大型解剖结构或心脏等移动目标的成像。人们研究了各种深度学习方法，大多数现有工作使用基于卷积神经网络（CNN）的架构作为重建主干，并与固定的（而非学习得到的）k空间欠采样模式相结合。在图像域和k空间中，基于CNN的架构由于其捕捉长程依赖关系的能力有限，可能并非重建的最佳选择。此外，固定的欠采样模式尽管易于实现，但可能无法实现最佳重建。最后，迄今为止，很少有深度学习模型利用动态MRI数据中的时间相关性来改进重建。为了弥补这些差距，我们提出了一种基于双域（图像和k空间）、基于Transformer的重建网络，并结合基于学习的欠采样，该网络接受具有时间相关性的MRI图像序列进行动态重建。我们将我们的模型称为DuDReTLU-net。我们针对完全采样的真实数据集对网络进行端到端训练。对在不同因子（5 - 100）下欠采样的人体心脏电影图像进行了测试。通过结构相似性指数、均方误差和峰值信噪比，对重建图像进行了视觉和定量评估。实验结果表明，在加速MRI重建中，DuDReTLU-net的性能优于现有最先进的方法（LOUPE、k-t SLR、BM3D-MRI）；消融研究表明，基于Transformer的重建在图像域和k空间中均优于基于CNN的重建；无论重建主干是（CNN还是Transformer），双域重建架构均优于单域重建架构；动态序列输入比单帧输入能带来更准确的重建。我们期望我们的结果能鼓励在加速MRI重建领域进一步研究双域架构、基于Transformer的架构以及基于学习的欠采样的应用。该项目的代码可在https://github.com/william2***3/dual-domain-mri-recon-nets免费获取（Hong等人，2022年）。
