Name
Title | ||
|---|---|---|
Field-inhomogeneity-corrected low-rank filtering of magnetic resonance spectroscopic imaging data. |
Abstract | ||
|---|---|---|
Low signal-to-noise ratio has been a major problem in magnetic resonance spectroscopic imaging (MRSI). A low-rank approximation based denoising method has been recently proposed to address this problem by exploiting the partial separability properties of MRSI data. However, field inhomogeneity, an unavoidable complication in practice, can violate the partial separability assumption and thus degrade the denoising performance of the low-rank filtering method. This paper presents a field-inhomogeneity-corrected low-rank filtering method to achieve more robust denoising of practical MRSI data. In vivo experiment results have been used to demonstrate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/EMBC.2014.6945098 | EMBC |
Keywords | Field | DocType |
field inhomogeneity corrected low rank filtering,low rank filtering method,mrsi data partial separability properties,magnetic resonance spectroscopy,signal-noise ratio,low rank approximation based denoising method,image denoising,biomedical mri,denoising performance,filtering theory,magnetic resonance spectroscopic imaging data,medical image processing | Computer vision,Computer science,Filter (signal processing),Artificial intelligence,Magnetic resonance spectroscopic imaging,Nuclear magnetic resonance | Conference |
Volume | ISSN | Citations |
2014 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Liu | 1 | 197 | 30.85 |
| Chao Ma | 2 | 9 | 1.89 |
| Bryan Clifford | 3 | 2 | 1.38 |
| Fan Lam | 4 | 50 | 9.14 |
| Curtis L. Johnson | 5 | 21 | 4.28 |
| Zhi-Pei Liang | 6 | 522 | 64.94 |