Fingerprint Representation of Metabolite Magnetic Resonance Spectroscopy with Deep Learning

Motivation: One of the major challenges for spectral fitting is the modeling of background signals. Goal(s): Develop a deep learning model for quantitative detection of in vivo metabolites without relying on spectral fitting. Approach: Spectral fingerprint representation is achieved by combining manifold learning and representation learning, with the tasks that include predicting metabolite concentrations, transverse relaxation times, and reconstructing individual metabolite signals. Results: The t-SNE map illustrates that metabolites can be clustered based on the fingerprints generated by the model. The predicted metabolite concentrations and relaxation T2s agree with those found in the literature. The spectral background or unregistered signals are effectively filtered out. Impact: The deep learning model demonstrates high practical viability for the quantification of metabolite concentrations and relaxation T2s. It essentially searches for learned spectral fingerprints instead of relying on spectral fitting, the latter involves modeling all signals contained in the data.