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.

翻译：磁共振成像(MRI)在诊所制作高分辨率图像以供诊断非常重要,但获取时间长于高分辨率图像。深学习基磁共振超分辨率方法可以减少扫描时间,而无需复杂的序列编程,但由于培训数据和测试数据之间的差异,可能会创造更多的文物。数据一致性层可以改善深层学习结果,但需要原始的 k- 空间数据。在这项工作中,我们提出了一个基于规模图像的数据一致性深层学习超分辨率方法,以提高超分辨率图像的质量,而没有原始的 k- 空间数据。我们的实验表明,拟议方法可以改进超分辨率图像的NRMSE和SSIM,而没有数据一致性模块的进化神经网络块。