Lipid Suppression in CSI with Spatial Priors and Highly Undersampled Peripheral k-space - Bilgic, Berkin (2012)
Mapping (1)H brain metabolites using chemical shift imaging (CSI) is hampered by the presence of subcutaneous lipid signals, which contaminate the metabolites by ringing due to limited spatial resolution. Even though CSI at spatial resolution high enough to mitigate the lipid artifacts is infeasible due to signal-to-noise (SNR) constraints on the metabolites, the lipid signals have orders of magnitude higher concentration, which enables the collection of high-resolution lipid maps with adequate SNR. The previously proposed dual-density approach exploits this high-SNR property of the lipid layer to suppress truncation artifacts using high-resolution lipid maps. Another recent approach for lipid suppression makes use of the fact that metabolite and lipid spectra are approximately orthogonal, and seeks sparse metabolite spectra when projected onto lipid-basis functions. The present work combines and extends the dual-density approach and the lipid-basis penalty, while estimating the high-resolution lipid image from 2-average k-space data to incur minimal increase on the scan time. Further, we exploit the spectral-spatial sparsity of the lipid ring and propose to estimate it from substantially undersampled (acceleration R = 10 in the peripheral k-space) 2-average in vivo data using compressed sensing, and still obtain improved lipid suppression relative to using dual-density or lipid-basis penalty alone.