Meeting Banner
Abstract #2785

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

Wen Wei1,2,3, Emilie Poirion2, Benedetta Bodini2, Stanley Durrleman2,3, Olivier Colliot2,3, Bruno Stankoff2, and Nicholas Ayache1

1Asclepios project-team, Inria, Sophia Antipolis, France, 2Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du cerveau et la moelle (ICM), AP-HP-Hôpital Pitié-Salpêtrière, Paris, France, 3Aramis project-team, Inria, Paris, France

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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here