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Abstract #0517

MR SPECTROSCOPY ARTIFACT REMOVAL WITH U-NET CONVOLUTIONAL NEURAL NETWORK

Nima Hatami1, Hélène Ratiney1, and Michaël Sdika1

1CREATIS, CNRS UMR 5220, Lyon, France

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.

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