Recent years have witnessed novel applications of machine learning in radiology. Developing robust machine learning based methods for removing spectral artifacts and reconstructing the intact metabolite spectrum is an open challenge in MR spectroscopy (MRS). We had shown autoencoder models reconstruct metabolite spectrum from unsuppressed water spectrum for short TE with relatively high SNR. In this work we presents an autoencoder model with feature fusion method to extract the shallow and deep features from a water unsuppressed 1H MR spectrum. The model learns to map the extracted feature to a latent code and reconstruct the intact metabolite spectrum
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