OBJECTIVE: To obtain reliable spectral estimation from magnetic resonance spectroscopic imaging (MRSI) data. METHODS: The proposed method takes advantage of prior knowledge: 1) along the spectral dimension in the form of spectral bases, and 2) along the spatial dimensions in the form of spatial regularizations (e.g., smoothness or transform sparsity) and jointly estimates parameters from all the voxels. RESULTS: Simulation and in vivo studies have been performed to demonstrate the performance of the proposed method. A Cramér-Rao-bound-based analysis is also provided. CONCLUSION: Incorporation of both spatial and spectral constraints can significantly improve spectral quantification of MRSI data. SIGNIFICANCE: The proposed method is expected to be useful for various quantitative MRSI studies.
OBJECTIVE: To obtain reliable spectral estimation from magnetic resonance spectroscopic imaging (MRSI) data. METHODS: The proposed method takes advantage of prior knowledge: 1) along the spectral dimension in the form of spectral bases, and 2) along the spatial dimensions in the form of spatial regularizations (e.g., smoothness or transform sparsity) and jointly estimates parameters from all the voxels. RESULTS: Simulation and in vivo studies have been performed to demonstrate the performance of the proposed method. A Cramér-Rao-bound-based analysis is also provided. CONCLUSION: Incorporation of both spatial and spectral constraints can significantly improve spectral quantification of MRSI data. SIGNIFICANCE: The proposed method is expected to be useful for various quantitative MRSI studies.
Authors: B Michael Kelm; Frederik O Kaster; Anke Henning; Marc-André Weber; Peter Bachert; Peter Boesiger; Fred A Hamprecht; Bjoern H Menze Journal: NMR Biomed Date: 2011-04-28 Impact factor: 4.044
Authors: Lihong Tang; Yibo Zhao; Yudu Li; Rong Guo; Bryan Clifford; Georges El Fakhri; Chao Ma; Zhi-Pei Liang; Jie Luo Journal: Magn Reson Med Date: 2020-07-29 Impact factor: 4.668