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.

Changes in mouse brain metabolism following a convulsive dose of soman: a proton HRMAS NMR study.

Soman, an irreversible organophosphorus cholinesterase inhibitor, induces status epilepticus and, in sensitive brain areas, seizure-related brain damage (e.g. brain edema and neuronal loss).

Quantitation with QUEST of brain HRMAS-NMR signals: application to metabolic disorders in experimental epileptic seizures.

Quantitation of High Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) signals enables establishing reference metabolite profiles of ex vivo tissues. Signals are often contaminated by a background signal originating mainly from macromolecules and lipids and by residual water which hampers proper quantitation. We show that automatic quantitation of HRMAS signals, even in the presence of a background, can be achieved by the semi-parametric algorithm QUEST based on prior knowledge of a metabolite basis-set.

Influence of measured and simulated basis sets on metabolite concentration estimates.

By quantification of brain metabolites, localized brain proton MRS can non-invasively provide biochemical information from distinct regions of the brain. Quantification of short-TE signals is usually based on a metabolite basis set. The basis set can be obtained by two approaches: (1) by measuring the signals of metabolites in aqueous solution; (2) by quantum-mechanically simulating the theoretical metabolite signals.

Semi-parametric estimation in magnetic resonance spectroscopy: automation of the disentanglement procedure.

Semi-parametric disentanglement of parametric parts from non-parametric parts of a signal is a universal problem. This study concerns estimation of metabolite concentrations from in vivo Magnetic Resonance Spectroscopy (MRS) signals. Due to in vivo conditions, so-called macro-molecules contribute non-parametric components to the signals.