论文标题
用于医学图像的基于变压器和GAN的超分辨率重建网络
Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images
论文作者
论文摘要
由于有必要获得具有最小辐射剂量的高质量图像，例如在低场磁共振成像中，医学成像中的超分辨率重建变得越来越流行（MRI）。但是，由于医学成像的复杂性和高度审美要求，图像超分辨率重建仍然是一个困难的挑战。在本文中，我们提供了一种基于学习的策略，用于利用变压器和生成对抗网络（T-GAN）重建低分辨率的医学图像。集成系统可以提取更精确的纹理信息，并通过将变压器成功地插入生成的对抗网络以进行图片重建之后，通过全局图像匹配，通过全局图像匹配提取更精确的纹理信息。此外，我们将内容损失，对抗性损失和对抗性特征损失的组合加权为在训练我们提出的T-GAN期间的最终多任务损失函数。与诸如PSNR和SSIM之类的既定措施相比，我们建议的T-GAN实现了最佳性能，并在MRI扫描膝盖和腹部的MRI扫描图像的超分辨率重建中恢复了更多纹理特征。
Because of the necessity to obtain high-quality images with minimal radiation doses, such as in low-field magnetic resonance imaging, super-resolution reconstruction in medical imaging has become more popular (MRI). However, due to the complexity and high aesthetic requirements of medical imaging, image super-resolution reconstruction remains a difficult challenge. In this paper, we offer a deep learning-based strategy for reconstructing medical images from low resolutions utilizing Transformer and Generative Adversarial Networks (T-GAN). The integrated system can extract more precise texture information and focus more on important locations through global image matching after successfully inserting Transformer into the generative adversarial network for picture reconstruction. Furthermore, we weighted the combination of content loss, adversarial loss, and adversarial feature loss as the final multi-task loss function during the training of our proposed model T-GAN. In comparison to established measures like PSNR and SSIM, our suggested T-GAN achieves optimal performance and recovers more texture features in super-resolution reconstruction of MRI scanned images of the knees and belly.
