REDUCED LIPID CONTAMINATION IN IN-VIVO H-1 MRSI USING TIME-DOMAIN FITTING AND NEURAL-NETWORK CLASSIFICATION

It is a well-known problem that metabolite maps, reconstructed from in vivo H-1 MRSI data sets, may suffer from contamination caused by the presence of strong lipid signals. In the present investigation, the lipid problem was addressed by applying specific signal processing and data-analysis techniques, combined with pattern recognition based on the concept of the artificial neural network. In order to arrive at images, cleaned from lipid artifacts, we have applied our previously introduced iterative and noniterative time-domain fitting procedures. Furthermore, reduction in computational time of the image reconstructions could be realized by using information provided by a neural network classification of the spectra, calculated from the MRSI data sets.