T1 and T2 Mapping Using Highly Sparse Unsuppressed Water Signals from MRSI Scans with Generalized Series-Assisted Low-Rank Tensor Modelling

MR spectroscopic imaging (MRSI) without water suppression provides a unique opportunity to use the unsuppressed water spectroscopic signals for T1 and T2 mapping. This work presents a new image reconstruction method for reconstructing the T1/T2 maps from the highly sparse MRSI data. This method uses a novel generalized series-assisted low-rank tensor model to absorb the high-quality reference MRSI images to constrain the spatial-spectral-parametric variations. Experimental results demonstrated very encouraging reconstruction performance.