Name
Abstract | ||
|---|---|---|
Compressed sensing for magnetic resonance imaging (CS-MRI) exploits image sparsity properties to reconstruct MRI from very few Fourier k-space measurements. The goal is to minimize any structural errors in the reconstruction that could have a negative impact on its diagnostic quality. To this end, we propose a deep error correction network (DECN) for CS-MRI. The DECN model consists of three parts, which we refer to as modules: a guide, or template, module, an error correction module, and a data fidelity module. Existing CS-MRI algorithms can serve as the template module for guiding the reconstruction. Using this template as a guide, the error correction module learns a convolutional neural network (CNN) to map the k-space data in a way that adjusts for the reconstruction error of the template image. Our experimental results show the proposed DECN CS-MRI reconstruction framework can considerably improve upon existing inversion algorithms by supplementing with an error-correcting CNN. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Fidelity,Pattern recognition,Convolutional neural network,Inversion (meteorology),Computer science,Reconstruction error,Error detection and correction,Fourier transform,Artificial intelligence,Compressed sensing |
DocType | Volume | Citations |
Journal | abs/1803.08763 | 0 |
PageRank | References | Authors |
0.34 | 21 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liyan Sun | 1 | 7 | 3.16 |
| Zhiwen Fan | 2 | 29 | 3.15 |
| Yue Huang | 3 | 317 | 29.82 |
| Xinghao Ding | 4 | 591 | 52.95 |
| John Paisley | 5 | 1003 | 55.70 |