Name
Abstract | ||
|---|---|---|
This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is used in the time-consuming phase-encoding direction to capture high-resolution image information, while permitting the image-folding problem dictated by the Poisson summation formula. To deal with the localization uncertainty due to image folding, a small number of low-frequency k-space data are added. Training the deep learning net involves input and output images that are pairs of the Fourier transforms of the subsampled and fully sampled k-space data. Our experiments show the remarkable performance of the proposed method; only 29% of the k-space data can generate images of high quality as effectively as standard MRI reconstruction with the fully sampled data. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1088/1361-6560/aac71a | PHYSICS IN MEDICINE AND BIOLOGY |
Keywords | Field | DocType |
magnetic resonance imaging,undersampling,deep learning,fast MRI | Small number,Computer vision,Computer science,Poisson summation formula,Input/output,Fourier transform,Sampling (statistics),Artificial intelligence,Deep learning | Journal |
Volume | Issue | ISSN |
63 | 13 | 0031-9155 |
Citations | PageRank | References |
19 | 0.77 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chang Min Hyun | 1 | 20 | 1.49 |
| Hwa Pyung Kim | 2 | 22 | 1.24 |
| Sung Min Lee | 3 | 19 | 1.11 |
| Sung-Chul Lee | 4 | 35 | 3.97 |
| Jin Keun Seo | 5 | 376 | 58.65 |