Learning-based Separation of Macromolecules and Metabolites in Ultrashort-TE FID MRSI with Auxiliary SE MRSI Data

Motivation: Macromolecules have significant spectral overlap with metabolites, confounding accurate quantification of metabolites in ultrashort-TE MRSI. Goal(s): To develop a novel method for effective and reliable separation of metabolites and macromolecules from ultrashort-TE FID MRSI data. Approach: We translated auxiliary macromolecule-free SE metabolite signals to FID signals using a learning-based approach. The translated metabolite reference was incorporated in the spectral model of FID MRSI data through generalized series modelling. Macromolecules signals were modelled with probabilistic subspaces. Results: The proposed method has been validated using numerical simulation and experimental data from healthy subjects and a tumor patient, producing encouraging results. Impact: This work provides a novel approach to exploiting the characteristic spectral features in FID and SE MRSI experiments for effective separation of metabolites and macromolecules.