Spectral estimation for magnetic resonance spectroscopic imaging with spatial sparsity constraints

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.