Abstract
This paper addresses the long-standing spectral quantitation problem in magnetic resonance spectroscopic imaging (MRSI). Although a large body of work has been done to develop robust solutions to the problem for practical MRSI applications, the problem remains challenging due to low signal-to-noise ratio (SNR) and model nonlinearity. Building on the existing work on the use of prior knowledge (in the form of spectral basis) for spectral estimation, this paper reformulates spectral quantitation as a joint estimation problem, and utilizes a regularization framework to enforce spatial constraints (e.g., spatial smoothness or transform sparsity) on the spectral parameters. Simulation and experimental results show that the proposed method, by exploiting both the spatial and spectral characteristics of the underlying signals, can significantly improve the estimation accuracy of the spectral parameters over state-of-the-art methods.
| Original language | English (US) |
|---|---|
| Title of host publication | 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015 |
| Publisher | IEEE Computer Society |
| Pages | 1482-1485 |
| Number of pages | 4 |
| Volume | 2015-July |
| ISBN (Electronic) | 9781479923748 |
| DOIs | |
| State | Published - Jul 21 2015 |
| Event | 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States Duration: Apr 16 2015 → Apr 19 2015 |
Other
| Other | 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 |
|---|---|
| Country | United States |
| City | Brooklyn |
| Period | 4/16/15 → 4/19/15 |
Fingerprint
Keywords
- Cramér-Rao bound
- MRSI
- sparsity constraint
- spatial regularization
- spectral estimation
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging
Cite this
Spectral estimation for magnetic resonance spectroscopic imaging with spatial sparsity constraints. / Ning, Qiang; Ma, Chao; Liang, Zhi-Pei.
2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015. Vol. 2015-July IEEE Computer Society, 2015. p. 1482-1485 7164157.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Spectral estimation for magnetic resonance spectroscopic imaging with spatial sparsity constraints
AU - Ning, Qiang
AU - Ma, Chao
AU - Liang, Zhi-Pei
PY - 2015/7/21
Y1 - 2015/7/21
N2 - This paper addresses the long-standing spectral quantitation problem in magnetic resonance spectroscopic imaging (MRSI). Although a large body of work has been done to develop robust solutions to the problem for practical MRSI applications, the problem remains challenging due to low signal-to-noise ratio (SNR) and model nonlinearity. Building on the existing work on the use of prior knowledge (in the form of spectral basis) for spectral estimation, this paper reformulates spectral quantitation as a joint estimation problem, and utilizes a regularization framework to enforce spatial constraints (e.g., spatial smoothness or transform sparsity) on the spectral parameters. Simulation and experimental results show that the proposed method, by exploiting both the spatial and spectral characteristics of the underlying signals, can significantly improve the estimation accuracy of the spectral parameters over state-of-the-art methods.
AB - This paper addresses the long-standing spectral quantitation problem in magnetic resonance spectroscopic imaging (MRSI). Although a large body of work has been done to develop robust solutions to the problem for practical MRSI applications, the problem remains challenging due to low signal-to-noise ratio (SNR) and model nonlinearity. Building on the existing work on the use of prior knowledge (in the form of spectral basis) for spectral estimation, this paper reformulates spectral quantitation as a joint estimation problem, and utilizes a regularization framework to enforce spatial constraints (e.g., spatial smoothness or transform sparsity) on the spectral parameters. Simulation and experimental results show that the proposed method, by exploiting both the spatial and spectral characteristics of the underlying signals, can significantly improve the estimation accuracy of the spectral parameters over state-of-the-art methods.
KW - Cramér-Rao bound
KW - MRSI
KW - sparsity constraint
KW - spatial regularization
KW - spectral estimation
UR - http://www.scopus.com/inward/record.url?scp=84944319933&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944319933&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2015.7164157
DO - 10.1109/ISBI.2015.7164157
M3 - Conference contribution
VL - 2015-July
SP - 1482
EP - 1485
BT - 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PB - IEEE Computer Society
ER -