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

Wen Wei 1, 2, 3 Emilie Poirion 3 Benedetta Bodini 3 Stanley Durrleman 1 Olivier Colliot 1 Bruno Stankoff 3 Nicholas Ayache 2
1 ARAMIS - Algorithms, models and methods for images and signals of the human brain
SU - Sorbonne Université, Inria de Paris, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
2 EPIONE - E-Patient : Images, données & mOdèles pour la médeciNe numériquE
CRISAM - Inria Sophia Antipolis - Méditerranée
3 ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
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.
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Submitted on : Monday, March 5, 2018 - 12:24:38 PM
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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⟩

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