Wang Shanshan, Su Zhenghang, Ying Leslie, Peng Xi, Zhu Shun, Liang Feng, Feng Dagan, Liang Dong
Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, CAS, Shenzhen, P.R.China.
School of Information Technologies, Guangdong University of Technology, Guangzhou, P.R. China.
Proc IEEE Int Symp Biomed Imaging. 2016 Apr;2016:514-517. doi: 10.1109/ISBI.2016.7493320. Epub 2016 Jun 16.
This paper proposes a deep learning approach for accelerating magnetic resonance imaging (MRI) using a large number of existing high quality MR images as the training datasets. An off-line convolutional neural network is designed and trained to identify the mapping relationship between the MR images obtained from zero-filled and fully-sampled k-space data. The network is not only capable of restoring fine structures and details but is also compatible with online constrained reconstruction methods. Experimental results on real MR data have shown encouraging performance of the proposed method for efficient and effective imaging.
本文提出了一种深度学习方法，该方法利用大量现有的高质量磁共振成像（MRI）图像作为训练数据集来加速MRI。设计并训练了一个离线卷积神经网络，以识别从零填充和全采样k空间数据获得的MR图像之间的映射关系。该网络不仅能够恢复精细结构和细节，还与在线约束重建方法兼容。对真实MR数据的实验结果表明，所提出的方法在高效成像方面具有令人鼓舞的性能。
