FLAIR MR Image Synthesis By Using 3D Fully Convolutional Networks for Multiple Sclerosis - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2018

FLAIR MR Image Synthesis By Using 3D Fully Convolutional Networks for Multiple Sclerosis

Abstract

Synopsis. Fluid-attenuated inversion recovery (FLAIR) MRI pulse sequence is used clinically and in research for the detection of WM lesions. However,in a clinical setting, some MRI pulse sequences can be missing because of patient or time constraints. We propose 3D fully convolutional neural networks to predict a FLAIR MRI pulse sequence from other MRI pulse sequences. We evaluate our approach on a real multiple sclerosis disease dataset by assessing the lesion contrast and by comparing our approach to other methods. Both the qualitative and quantitative results show that our method is competitive for FLAIR prediction.
Fichier principal
Vignette du fichier
ismrm_2018_wen_final.pdf (2.99 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01723070 , version 1 (05-03-2018)

Identifiers

  • HAL Id : hal-01723070 , version 1

Cite

Wen Wei, Emilie Poirion, Benedetta Bodini, Stanley Durrleman, Olivier Colliot, et al.. FLAIR MR Image Synthesis By Using 3D Fully Convolutional Networks for Multiple Sclerosis. ISMRM-ESMRMB 2018 - Joint Annual Meeting, Jun 2018, Paris, France. pp.1-6. ⟨hal-01723070⟩
398 View
468 Download

Share

Gmail Facebook X LinkedIn More