SLIPMAT: A pipeline for extracting tissue-specific spectral profiles from 1 H MR spectroscopic imaging data

1 H Magnetic Resonance Spectroscopy (MRS) is an important non-invasive tool for measuring brain metabolism, with numerous applications in the neuroscientific and clinical domains. In this work we present a new analysis pipeline (SLIPMAT), designed to extract high-quality, tissue-specific, spectral profiles from MR spectroscopic imaging data (MRSI). Spectral decomposition is combined with spatially dependant frequency and phase correction to yield high SNR white and grey matter spectra without partial-volume contamination. A subsequent series of spectral processing steps are applied to reduce unwanted spectral variation, such as baseline correction and linewidth matching, before direct spectral analysis with machine learning and traditional statistical methods. The method is validated using a 2D semi-LASER MRSI sequence, with a 5-minute duration, from data acquired in triplicate across 8 healthy participants. Reliable spectral profiles are confirmed with principal component analysis, revealing the importance of total-choline and scyllo-inositol levels in distinguishing between individuals - in good agreement with our previous work. Furthermore, since the method allows the simultaneous measurement of metabolites in grey and white matter, we show the strong discriminative value of these metabolites in both tissue types for the first time. In conclusion, we present a novel and time efficient MRSI acquisition and processing pipeline, capable of detecting reliable neuro-metabolic differences between healthy individuals, and suitable for the sensitive neurometabolic profiling of in-vivo brain tissue.

Automated summary

In this work, a new analysis pipeline called SLIPMAT is presented for extracting high-quality, tissue-specific, spectral profiles from MR spectroscopic imaging data (MRSI). The method combines spectral decomposition with spatially dependent frequency and phase correction to obtain high signal-to-noise ratio white and grey matter spectra. It includes spectral processing steps to reduce unwanted spectral variation and direct spectral analysis using machine learning and statistical methods. The method is validated using data from 8 healthy participants, showing reliable spectral profiles and the discriminative value of metabolites in both grey and white matter.