Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using three-dimensional fully convolutional networks for multiple sclerosis

Wen Wei; Emilie Poirion; Benedetta Bodini; Stanley Durrleman; Olivier Colliot; Bruno Stankoff; Nicholas Ayache

doi:10.1117/1.JMI.6.1.014005

19 February 2019 Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using three-dimensional fully convolutional networks for multiple sclerosis

Wen Wei, Emilie Poirion, Benedetta Bodini, Stanley Durrleman, Olivier Colliot, Bruno Stankoff, Nicholas Ayache

Author Affiliations +

Journal of Medical Imaging, Vol. 6, Issue 1, 014005 (February 2019). https://doi.org/10.1117/1.JMI.6.1.014005

Abstract

Multiple sclerosis (MS) is a white matter (WM) disease characterized by the formation of WM lesions, which can be visualized by magnetic resonance imaging (MRI). The fluid-attenuated inversion recovery (FLAIR) MRI pulse sequence is used clinically and in research for the detection of WM lesions. However, in clinical settings, some MRI pulse sequences could be missed because of various constraints. The use of the three-dimensional fully convolutional neural networks is proposed to predict FLAIR pulse sequences from other MRI pulse sequences. In addition, the contribution of each input pulse sequence is evaluated with a pulse sequence-specific saliency map. This approach is tested on a real MS image dataset and evaluated by comparing this approach with other methods and by assessing the lesion contrast in the synthetic FLAIR pulse sequence. Both the qualitative and quantitative results show that this method is competitive for FLAIR synthesis.

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Wen Wei, Emilie Poirion, Benedetta Bodini, Stanley Durrleman, Olivier Colliot, Bruno Stankoff, and Nicholas Ayache "Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using three-dimensional fully convolutional networks for multiple sclerosis," Journal of Medical Imaging 6(1), 014005 (19 February 2019). https://doi.org/10.1117/1.JMI.6.1.014005

Received: 14 September 2018; Accepted: 29 January 2019; Published: 19 February 2019

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