RESUMEN
PURPOSE: To improve signal-to-noise ratio (SNR) for high-resolution spectroscopic imaging using a subspace-based technique known as SPectroscopic Imaging by exploiting spatiospectral CorrElation (SPICE). METHODS: The proposed method is based on a union-of-subspaces model of MRSI signals, which exploits the partial separability properties of water, lipid, baseline and metabolite signals. Enabled by this model, a special scheme is used for accelerated data acquisition, which includes a double-echo CSI component used to collect a "training" dataset (for determination of the basis functions) and a short-TE EPSI component used to collect a sparse "imaging" dataset (for determination of the overall spatiospectral distributions). A set of signal processing algorithms are developed to remove the water and lipid signals and jointly reconstruct the metabolite and baseline signals. RESULTS: In vivo 1 H-MRSI results show that the proposed method can effectively remove the remaining water and lipid signals from sparse MRSI data acquired at 20 ms TE. Spatiospectral distributions of metabolite signals at 2 mm in-plane resolution with good SNR were obtained in a 15.5 min scan. CONCLUSIONS: The proposed method can effectively remove nuisance signals and reconstruct high-resolution spatiospectral functions from sparse data to make short-TE SPICE possible. The method should prove useful for high-resolution 1 H-MRSI of the brain. Magn Reson Med 77:467-479, 2017. © 2016 International Society for Magnetic Resonance in Medicine.