MR SPECTROSCOPY ARTIFACT REMOVAL WITH U-NET CONVOLUTIONAL NEURAL NETWORK

Nima Hatami 1 Hélène Ratiney 2 Michaël Sdika 1
1 Images et Modèles
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
2 RMN et optique : De la mesure au biomarqueur
CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
Abstract : 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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https://hal.archives-ouvertes.fr/hal-02129946
Contributor : Béatrice Rayet <>
Submitted on : Wednesday, May 15, 2019 - 2:21:59 PM
Last modification on : Wednesday, November 20, 2019 - 3:14:58 AM

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  • HAL Id : hal-02129946, version 1

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Nima Hatami, Hélène Ratiney, Michaël Sdika. MR SPECTROSCOPY ARTIFACT REMOVAL WITH U-NET CONVOLUTIONAL NEURAL NETWORK. 27th Annual meeting of the ISMRM, May 2019, Montreal, Canada. ⟨hal-02129946⟩

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