Spectral estimation with spatio-spectral constraints for magnetic resonance spectroscopic imaging

Magnetic resonance spectroscopic imaging (MRSI);spectral estimation;spatial regularization;sparsity constraint;Cramer-Rao Bound

Ning, Qiang ; Liang ; Zhi-Pei

关键词: Magnetic resonance spectroscopic imaging (MRSI);    spectral estimation;    spatial regularization;    sparsity constraint;    Cramer-Rao Bound;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/89025/NING-THESIS-2015.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Magnetic resonance spectroscopic imaging (MRSI) is a promising tool to acquire in vivo biochemical information, and spectral estimation (quantification) of MRSI data is an important step towards quantitative studies. Although a large body of work has been done on spectral estimation over the past decades, it remains challenging due to model nonlinearity and extremely low signal-to-noise ratio (SNR). Building on the existing methods which effectively incorporate spectral prior knowledge in the form of basis functions, this work addresses the spectral estimation problem by incorporating both spectral and spatial prior information. Specifically, we jointly estimate the spectra over all the voxels of interest, incorporating prior spatial information in a regularization framework. The effectiveness of the proposed method has been evaluated using both simulated and experimental data. A theoretical analysis based on Cramer-Rao Bound is proposed to further assess the performance improvement of the proposed method over state-of-the-art methods. The proposed spectral estimation method should prove useful in various MRSI studies.