An efficient sequence for fetal brain imaging at 3T with enhanced T1 contrast and motion robustness

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Ultrafast single-shot T2-weighted images are common practice in fetal MR exams. However, there is limited experience with fetal T1-weighted acquisitions. This study aims at establishing a robust framework that allows fetal T1-weighted scans to be routinely acquired in utero at 3T.

A 2D gradient echo sequence with an adiabatic inversion was optimized to be robust to fetal motion and maternal breathing optimizing grey/white matter contrast at the same time. This was combined with slice to volume registration and super resolution methods to produce volumetric reconstructions. The sequence was tested on 22 fetuses.

Optimized grey/white matter contrast and robustness to fetal motion and maternal breathing were achieved. Signal from cerebrospinal fluid (CSF) and amniotic fluid was nulled and 0.75 mm isotropic anatomical reconstructions of the fetal brain were obtained using slice-to-volume registration and super resolution techniques. Total acquisition time for a single stack was 56 s, all acquired during free breathing. Enhanced sensitivity to normal anatomy and pathology with respect to established methods is demonstrated. A direct comparison with a 3D spoiled gradient echo sequence and a controlled motion experiment run on an adult volunteer are also shown.

This paper describes a robust framework to perform T1-weighted acquisitions and reconstructions of the fetal brain in utero. Magn Reson Med, 2017. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Original languageEnglish
JournalMagnetic Resonance in Medicine
Early online date28 Nov 2017
Publication statusE-pub ahead of print - 28 Nov 2017


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