论文标题
使用无监督的多尺度生成对抗网络的动脉自旋标记MR成像的超级分辨率
Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network
论文作者
论文摘要
动脉自旋标记（ASL）磁共振成像（MRI）是一种强大的成像技术，可以定量测量大脑血流（CBF）。但是，由于与整个组织体积相比，只有一小部分的血液被标记，因此常规的ASL遭受了低信噪比（SNR），空间分辨率差和较长的获取时间。在本文中，我们通过无监督训练提出了一种基于多尺度生成对抗网络（GAN）的超分辨率方法。该网络仅需要低分辨率（LR）ASL图像本身进行训练，而T1加权图像作为解剖学先验。不需要培训对或预训练。添加了低通滤光导向物品作为额外损失，以抑制LR ASL图像中的噪声干扰。训练网络后，通过向上一层的发电机提供UPS采样的LR ASL图像和相应的T1加权图像来生成超分辨率（SR）图像。通过比较使用正常分辨率（NR）ASL图像（5.5 min获取）和高分辨率（HR）ASL图像（44 min faceisition）将峰值信噪比（PSNR）和结构相似性指数（SSIM）进行比较来评估所提出方法的性能。与最接近的，线性和样条插值方法相比，所提出的方法恢复了更详细的结构信息，以视觉上减少图像噪声，并在使用HR ASL图像作为地面真实度时达到最高的PSNR和SSIM。
Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is a powerful imaging technology that can measure cerebral blood flow (CBF) quantitatively. However, since only a small portion of blood is labeled compared to the whole tissue volume, conventional ASL suffers from low signal-to-noise ratio (SNR), poor spatial resolution, and long acquisition time. In this paper, we proposed a super-resolution method based on a multi-scale generative adversarial network (GAN) through unsupervised training. The network only needs the low-resolution (LR) ASL image itself for training and the T1-weighted image as the anatomical prior. No training pairs or pre-training are needed. A low-pass filter guided item was added as an additional loss to suppress the noise interference from the LR ASL image. After the network was trained, the super-resolution (SR) image was generated by supplying the upsampled LR ASL image and corresponding T1-weighted image to the generator of the last layer. Performance of the proposed method was evaluated by comparing the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) using normal-resolution (NR) ASL image (5.5 min acquisition) and high-resolution (HR) ASL image (44 min acquisition) as the ground truth. Compared to the nearest, linear, and spline interpolation methods, the proposed method recovers more detailed structure information, reduces the image noise visually, and achieves the highest PSNR and SSIM when using HR ASL image as the ground-truth.
