MR SPECTROSCOPY ARTIFACT REMOVAL WITH U-NET CONVOLUTIONAL NEURAL NETWORK

Nima Hatami (1, 2) , Hélène Ratiney (3) , Michaël Sdika (1, 2)

1 Images et Modèles
2 MYRIAD - Modeling & analysis for medical imaging and Diagnosis
3 RMN et optique : De la mesure au biomarqueur

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.

Domaines: Traitement du signal et de l'image [eess.SP]; Intelligence artificielle [cs.AI]; Imagerie

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⟩