Name
Title | ||
|---|---|---|
Referenceless distortion correction of gradient-echo echo-planar imaging under inhomogeneous magnetic fields based on a deep convolutional neural network. |
Abstract | ||
|---|---|---|
Single-shot gradient-echo echo-planar imaging (GE-EPI) plays a significant role in applications where high temporal resolution is necessary. However, GE-EPI is susceptible to inhomogeneous magnetic fields that will cause image distortion. Most existing methods either need additional acquisitions for field mapping or cannot correct the distortion at high field. Here, we propose a new algorithm based on a deep convolutional neural network (CNN) to solve this problem without additional acquisitions. The residual learning and the cascaded structure improved the performance of the CNN on distortion correction. A simulated dataset was used for training. The simulated and experimental results demonstrate that the proposed method can correct the image distortion caused by field inhomogeneity. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.compbiomed.2018.07.010 | Computers in Biology and Medicine |
Keywords | Field | DocType |
Magnetic resonance imaging,Inhomogeneous magnetic field,Distortion correction,Convolutional neural network,Residual learning | Computer vision,Residual,Magnetic field,Distortion correction,Computer science,Convolutional neural network,Echo-planar imaging,High field,Artificial intelligence,Distortion,Temporal resolution | Journal |
Volume | ISSN | Citations |
100 | 0010-4825 | 2 |
PageRank | References | Authors |
0.39 | 25 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pu Liao | 1 | 2 | 0.39 |
| Jun Zhang | 2 | 40 | 6.65 |
| Kun Zeng | 3 | 116 | 5.48 |
| Yonggui Yang | 4 | 2 | 1.06 |
| Shuhui Cai | 5 | 14 | 6.10 |
| Gang Guo | 6 | 2 | 3.43 |
| Congbo Cai | 7 | 10 | 5.90 |