TY - GEN
T1 - Spectral estimation for magnetic resonance spectroscopic imaging with spatial sparsity constraints
AU - Ning, Qiang
AU - Ma, Chao
AU - Liang, Zhi Pei
N1 - Publisher Copyright:
© 2015 IEEE.
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
AN - SCOPUS:84944319933
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1482
EP - 1485
BT - 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PB - IEEE Computer Society
T2 - 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Y2 - 16 April 2015 through 19 April 2015
ER -