MR SPECTROSCOPY ARTIFACT REMOVAL WITH U-NET CONVOLUTIONAL NEURAL NETWORK
Résumé
In in vivo MR spectroscopy, a variety of artifacts may affect spectral quality and are not easy to detect and remove by non-experts. A U-NET
architecture is proposed to remove artifacts from MRS spectra with deep learning. The principle is demonstrated on synthetic simulated data
mimicking in vivo conditions.