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Abstract #2416

Quantification of Unsuppressed Water Spectrum using Autoencoder with Feature Fusion

Marcia Sahaya Louis1,2, Eduardo Coello2, Huijun Liao2, Ajay Joshi1, and Alexander P Lin2
1Boston University, Boston, MA, United States, 2Brigham and Women's hospital, Boston, MA, United States

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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