Reducing motion sensitivity in 3D high-resolution T 2 -weighted MRI by navigator-based motion and nonlinear magnetic field correction

T *-weighted gradient echo (GRE) MRI at high field is uniquely sensitive to the magnetic properties of tissue and allows the study of brain and vascular anatomy at high spatial resolution. However, it is also sensitive to B field changes induced by head motion and physiological processes such as the...

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Veröffentlicht in:NeuroImage (Orlando, Fla.) Fla.), 2020-02, Vol.206, p.116332
Hauptverfasser: Liu, Jiaen, van Gelderen, Peter, de Zwart, Jacco A, Duyn, Jeff H
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Sprache:eng
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Zusammenfassung:T *-weighted gradient echo (GRE) MRI at high field is uniquely sensitive to the magnetic properties of tissue and allows the study of brain and vascular anatomy at high spatial resolution. However, it is also sensitive to B field changes induced by head motion and physiological processes such as the respiratory cycle. Conventional motion correction techniques do not take these field changes into account, and consequently do not fully recover image quality in T *-weighted MRI. Here, a novel approach was developed to address this by monitoring the B field with a volumetric EPI phase navigator. The navigator was acquired at a shorter echo time than that of the (higher resolution) T *-weighted GRE imaging data and accelerated with parallel imaging for high temporal resolution. At 4 ​mm isotropic spatial resolution and 0.54 ​s temporal resolution, the accuracy for estimation of rotation and translation was better than 0.2° and 0.1 ​mm, respectively. The 10% and 90% percentiles of B measurement error using the navigator were -1.8 and 1.5 Hz  at 7 T, respectively. A fast retrospective reconstruction algorithm correcting for both motion and nonlinear B changes was also developed. The navigator and reconstruction algorithm were evaluated in correcting motion-corrupted high-resolution T *-weighted GRE MRI on healthy human subjects at 7 ​T. Excellent image quality was demonstrated with the proposed correction method.
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2019.116332