The brainstem in multiple sclerosis: MR identification of tracts and nuclei damage

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Abstract

Objective: To evaluate the 3D Fast Gray Acquisition T1 Inversion Recovery (FGATIR) sequence for MRI identification of brainstem tracts and nuclei damage in multiple sclerosis (MS) patients. Methods: From april to december 2020, 10 healthy volunteers and 50 patients with remitted-relapsing MS (58% female, mean age 36) underwent MR imaging in the Neuro-imaging department of the C.H.N.O. des Quinze-Vingts, Paris, France. MRI was achieved on a 3 T system (MAGNETOM Skyra) using a 64-channel coil. 3D FGATIR sequence was first performed on healthy volunteers to classify macroscopically identifiable brainstem structures. Then, FGATIR was assessed in MS patients to locate brainstem lesions detected with Proton Density/T2w (PD/T2w) sequence. Results: In healthy volunteers, FGATIR allowed a precise visualization of tracts and nuclei according to their myelin density. Including FGATIR in MR follow-up of MS patients helped to identify structures frequently involved in the inflammatory process. Most damaged tracts were the superior cerebellar peduncle and the transverse fibers of the pons. Most frequently affected nuclei were the vestibular nuclei, the trigeminal tract, the facial nerve and the solitary tract. Conclusion: Combination of FGATIR and PD/T2w sequences opened prospects to define MS elective injury in brainstem tracts and nuclei, with particular lesion features suggesting variations of the inflammatory process within brainstem structures. In a further study, hypersignal quantification and microstructure information should be evaluated using relaxometry and diffusion tractography. Technical improvements would bring novel parameters to train an artificial neural network for accurate automated labeling of MS lesions within the brainstem.

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Nguyen, T. H., Vaussy, A., Le Gaudu, V., Aboab, J., Espinoza, S., Curajos, I., … Habas, C. (2021). The brainstem in multiple sclerosis: MR identification of tracts and nuclei damage. Insights into Imaging, 12(1). https://doi.org/10.1186/s13244-021-01101-7

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