Abstract #2785

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

Wen Wei^1,2,3, Emilie Poirion², Benedetta Bodini², Stanley Durrleman^2,3, Olivier Colliot^2,3, 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.

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