Quantification of human brain metabolites from in vivo 1H NMR magnitude spectra using automated artificial neural network analysis.

Long echo time (TE=270 ms) in vivo proton NMR spectra resembling human brain metabolite patterns were simulated for lineshape fitting (LF) and quantitative artificial neural network (ANN) analyses. A set of experimental in vivo 1H NMR spectra were first analyzed by the LF method to match the signal-to-noise ratios and linewidths of simulated spectra to those in the experimental data. The performance of constructed ANNs was compared for the peak area determinations of choline-containing compounds (Cho), total creatine (Cr), and N-acetyl aspartate (NAA) signals using both manually phase-corrected and magnitude spectra as inputs.

Automated quantification of human brain metabolites by artificial neural network analysis from in vivo single-voxel 1H NMR spectra.

A real-time automated way of quantifying metabolites from in vivo NMR spectra using an artificial neural network (ANN) analysis is presented. The spectral training and test sets for ANN containing peaks at the chemical shift ranges resembling long echo time proton NMR spectra from human brain were simulated. The performance of the ANN constructed was compared with an established lineshape fitting (LF) analysis using both simulated and experimental spectral data as inputs.

Application of self-organizing maps for the detection and classification of human blood plasma lipoprotein lipid profiles on the basis of 1H NMR spectroscopy data.

Efficient and relevant classification of clinical findings, i.e. diagnostic decision making, poses a major challenge in medicine.