论文标题
深切的关注Wasserstein生成的对抗网络，用于MRI重建，并具有反复的上下文意识
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness
论文作者
论文摘要
传统的基于压缩传感的MRI（CS-MRI）重建的性能受其缓慢的迭代程序和噪声引起的人工制品的影响。尽管已经提出了许多基于深度学习的CS-MRI方法来减轻传统方法的问题，但在更高的加速因素下，它们无法获得更强大的结果。大多数基于深度学习的CS-MRI方法仍然无法完全从K空间中挖掘信息，从而导致MRI重建的结果不令人满意。在这项研究中，我们提出了一种新的基于深度学习的CS-MRI重建方法，以通过耦合Wasserstein生成对抗网络（WGAN）与复发性神经网络完全利用顺序MRI切片之间的关系。进一步开发细心单元使我们的模型能够为MRI数据重建更准确的解剖结构。通过在不同的MRI数据集上进行实验，我们证明了我们的方法不仅可以与最先进的方法相比获得更好的结果，而且还可以有效地减少重建过程中产生的残留噪声。
The performance of traditional compressive sensing-based MRI (CS-MRI) reconstruction is affected by its slow iterative procedure and noise-induced artefacts. Although many deep learning-based CS-MRI methods have been proposed to mitigate the problems of traditional methods, they have not been able to achieve more robust results at higher acceleration factors. Most of the deep learning-based CS-MRI methods still can not fully mine the information from the k-space, which leads to unsatisfactory results in the MRI reconstruction. In this study, we propose a new deep learning-based CS-MRI reconstruction method to fully utilise the relationship among sequential MRI slices by coupling Wasserstein Generative Adversarial Networks (WGAN) with Recurrent Neural Networks. Further development of an attentive unit enables our model to reconstruct more accurate anatomical structures for the MRI data. By experimenting on different MRI datasets, we have demonstrated that our method can not only achieve better results compared to the state-of-the-arts but can also effectively reduce residual noise generated during the reconstruction process.
