Name
Abstract | ||
|---|---|---|
The under-sampling trajectory design plays a key role in compressed sensing MRI. The traditional design scheme using probability density function (PDF) is based up observation on energy distribution in k-space rather than systematic optimization, which results in non-deterministic trajectory even with a fixed PDF. Guidance-based method like Bayesian inference scheme is always bothered with high computational complexity on entropy. In this paper, we study how to adaptively design an under-sampling trajectory in the context of CS with systematic optimization and small complexity. Simulation results conducted on images from different slices and dynamic sequence demonstrate the effectiveness of the proposed method by comparing the designed trajectory with those by traditional method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/EMBC.2012.6345874 | EMBC |
Keywords | Field | DocType |
undersampling trajectory design,data compression,medical signal processing,biomedical mri,sampling methods,compressed sensing mri,dynamic sequence,systematic optimization,traditional method comparison | Computer vision,Bayesian inference,Computer science,Artificial intelligence,Sampling (statistics),Data compression,Probability density function,Trajectory,Compressed sensing,Computational complexity theory,Energy distribution | Conference |
Volume | Issue | ISSN |
2012 | null | 1557-170X |
ISBN | Citations | PageRank |
978-1-4577-1787-1 | 5 | 0.45 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Duan-Duan Liu | 1 | 5 | 0.45 |
| Dong Liang | 2 | 47 | 10.50 |
| Xin Liu | 3 | 90 | 9.93 |
| Yuan-Ting Zhang | 4 | 160 | 27.01 |