论文标题
MRI超级分辨率的基于数据级图像的深度学习方法
Magnitude-image based data-consistent deep learning method for MRI super resolution
论文作者
论文摘要
磁共振成像（MRI）在临床中很重要，可以产生高分辨率图像进行诊断，但其获取时间持续了高分辨率图像。基于深度学习的MRI超级分辨率方法可以减少扫描时间而无需复杂的序列编程，但由于训练数据和测试数据之间的差异，可能会产生其他伪像。数据一致性层可以改善深度学习结果，但需要原始的K空间数据。在这项工作中，我们提出了基于幅度图像的数据一致性深度学习MRI超级分辨率方法，以提高超级分辨率图像的质量，而无需原始K空间数据。我们的实验表明，与没有数据一致性模块的同一卷积神经网络（CNN）块相比，提出的方法可以改善超级分辨率图像的NRMSE和SSIM。
Magnetic Resonance Imaging (MRI) is important in clinic to produce high resolution images for diagnosis, but its acquisition time is long for high resolution images. Deep learning based MRI super resolution methods can reduce scan time without complicated sequence programming, but may create additional artifacts due to the discrepancy between training data and testing data. Data consistency layer can improve the deep learning results but needs raw k-space data. In this work, we propose a magnitude-image based data consistency deep learning MRI super resolution method to improve super resolution images' quality without raw k-space data. Our experiments show that the proposed method can improve NRMSE and SSIM of super resolution images compared to the same Convolutional Neural Network (CNN) block without data consistency module.
