Itthi Chatnuntawech1, Berkin Bilgic1, Elfar Adalsteinsson2, 3
1EECS, Massachusetts Institute of Technology, Cambridge, MA, United States; 2EECS, MIT, Cambridge, MA, United States; 3Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, United States
In this work, a two-step model-based method that leads to an accurate reconstruction from undersampled spectroscopic imaging data is proposed. This method takes advantage of a fast water reference scan to estimate a subset of (non-linear) unknowns, leaving only a few, linear unknowns to be determined in the next step. Then, a regularized optimization problem with prior knowledge on the structure of the data is formulated to reconstruct the spectroscopic imaging data. This method reduces acquisition time by undersampling while preserving high reconstruction quality. The proposed method yields significantly lower root mean square error than that of the conventional method which finds the minimum-norm solution without regularization.