论文标题
快速MRI重建：变压器有多强大？
Fast MRI Reconstruction: How Powerful Transformers Are?
论文作者
论文摘要
磁共振成像（MRI）是一种广泛使用的非辐射性和非侵入性方法，用于对器官结构和代谢的临床询问，并具有固有的较长扫描时间。通过K空间底采样和基于深度学习的重建方法已被普及以加速扫描过程。这项工作着重于通过利用和比较不同的新型网络体系结构来调查强大的变压器对快速MRI的强大方法。特别是，引入了基于生成的对抗网络（GAN）的SWIN变压器（St-GAN）进行快速MRI重建。为了进一步保留边缘和纹理信息，还开发了边缘增强的基于GAN的基于GAN的SWIN变压器（EES-GAN）和纹理增强的基于GAN的Swin Transformer（TES-GAN），并在其中应用了双歧丝gan结构。我们根据评估指标PSNR，SSIM和FID比较了我们提出的基于GAN的变压器，独立的SWIN变压器和其他基于卷积神经网络的GAN模型。我们表明，变形金刚从不同的不足采样条件下的MRI重建很好地工作。 GAN对抗结构的利用可改善在采样30％或更高时重建图像的质量。该代码可在https://github.com/ayanglab/swinganmr上公开获取。
Magnetic resonance imaging (MRI) is a widely used non-radiative and non-invasive method for clinical interrogation of organ structures and metabolism, with an inherently long scanning time. Methods by k-space undersampling and deep learning based reconstruction have been popularised to accelerate the scanning process. This work focuses on investigating how powerful transformers are for fast MRI by exploiting and comparing different novel network architectures. In particular, a generative adversarial network (GAN) based Swin transformer (ST-GAN) was introduced for the fast MRI reconstruction. To further preserve the edge and texture information, edge enhanced GAN based Swin transformer (EES-GAN) and texture enhanced GAN based Swin transformer (TES-GAN) were also developed, where a dual-discriminator GAN structure was applied. We compared our proposed GAN based transformers, standalone Swin transformer and other convolutional neural networks based GAN model in terms of the evaluation metrics PSNR, SSIM and FID. We showed that transformers work well for the MRI reconstruction from different undersampling conditions. The utilisation of GAN's adversarial structure improves the quality of images reconstructed when undersampled for 30% or higher. The code is publicly available at https://github.com/ayanglab/SwinGANMR.
