Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using three-dimensional fully convolutional networks for multiple sclerosis
Year: 2019
Type: article
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.
Source: Journal of medical imaging
Institutions: Institut national de recherche en sciences et technologies du numérique, Université Côte d'Azur, Centre National de la Recherche Scientifique, Inserm, Sorbonne Université, Assistance Publique – Hôpitaux de Paris, Institut du Cerveau, ARAMIS: Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain sain et pathologique
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2917568709