Semi-parametric time-domain quantification of HR-MAS data from prostate tissue.

High Resolution--Magic Angle Spinning (HR-MAS) spectroscopy provides rich biochemical profiles that require accurate quantification to permit biomarker identification and to understand the underlying pathological mechanisms. Meanwhile, quantification of HR-MAS data from prostate tissue samples is challenging due to significant overlap between the resonant peaks, the presence of short T₂* metabolites such as citrate or polyamines (T₂ from 25 to 100 msec) and macromolecules, and variations in chemical shifts and T₂*s within a metabolite's spin systems. Since existing methods do not address these challenges completely, a new quantification method was developed and optimized for HR-MAS data acquired with an ultra short T(E) and over 30,000 data points. The proposed method, named HR-QUEST (High Resolution--QUEST), iteratively employs the QUEST time-domain semi-parametric strategy with a new model function that incorporates prior knowledge from whole and subdivided metabolite signals. With these features, HR-QUEST is able to independently fit the chemical shifts and T₂*s of a metabolite's spin systems, a necessity for HR-MAS data. By using the iterative fitting approach, it is able to account for significant contributions from macromolecules and to handle shorter T₂ metabolites, such as citrate and polyamines. After subdividing the necessary metabolite basis signals, the root mean square (RMS) of the residual was reduced by 52% for measured HR-MAS data from prostate tissue. Monte Carlo studies on simulated spectra with varied macromolecular contributions showed that the iterative fitting approach (6 iterations) coupled with inclusion of long T₂ macromolecule components in the basis set improve the quality of the fit, as assessed by the reduction of the RMS of the residual and of the RMS error of the metabolite signal estimate, by 27% and 71% respectively. With this optimized configuration, HR-QUEST was applied to measured HR-MAS prostate data and reliably quantified 16 metabolites and reference signals with estimated Cramér Rao Bounds ≤5%.