论文标题
使用未经地面训练的深CNN先验的MRI的图像重建
Image Reconstruction for MRI using Deep CNN Priors Trained without Groundtruth
论文作者
论文摘要
我们提出了一种新的插件竞争先验（PNP）的MR图像重建方法，该方法可以系统地执行数据一致性，同时还利用了深入学习的先验研究。我们的先验是通过未经任何无伪影地面真理训练的卷积神经网络（CNN）来指定的，从而从MR图像中删除了不足的采样伪像。将自由呼吸的MRI数据重建为十个呼吸阶段的结果表明，该方法可以从严重底面采样的测量值中形成高质量的4D图像，这些测量值对应于大约1和2分钟长度。结果还强调了该方法的竞争性能与几种流行替代方案相比，包括TGV正则化和传统的UNET3D。
We propose a new plug-and-play priors (PnP) based MR image reconstruction method that systematically enforces data consistency while also exploiting deep-learning priors. Our prior is specified through a convolutional neural network (CNN) trained without any artifact-free ground truth to remove undersampling artifacts from MR images. The results on reconstructing free-breathing MRI data into ten respiratory phases show that the method can form high-quality 4D images from severely undersampled measurements corresponding to acquisitions of about 1 and 2 minutes in length. The results also highlight the competitive performance of the method compared to several popular alternatives, including the TGV regularization and traditional UNet3D.
