Abstract #4433

vNav-QALAS: Motion robust 3D multi-parametric brain mapping with volumetric navigators

Pengcheng Xu^1,2,3, Shohei Fujita^2,3, Yohan Jun^2,3, Borjan Gagoski^3,4, Onur Afacan^3,5, Huafeng Liu¹, and Berkin Bilgic^2,3,6

¹College of Optical Science and Engineering, Zhejiang University, Hangzhou, China, ²Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, ³Department of Radiology, Harvard Medical School, Boston, MA, United States, ⁴Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States, ⁵Radiology, Boston Children's Hospital, Boston, MA, United States, ⁶Harvard/MIT Health Sciences and Technology, Massachusetts Institute of Technology, Boston, MA, United States

Synopsis

Keywords: Motion Correction, Motion Correction

Motivation: Subject motion during long, 3D-encoded multi-parametric MRI scans can significantly hamper the image quality of the reconstructed quantitative maps.

Goal(s): We propose a motion-robust quantitative brain mapping method, vNav-QALAS, that uses no additional hardware for head motion tracking.

Approach: We embedded low-resolution 3D volumetric EPI navigators (EPI-vNavs) in the QALAS sequence, enabling head motion estimates every TR. The motion trajectory was then incorporated into a retrospectively motion corrected parallel imaging reconstruction.

Results: The EPI-vNavs enable a motion-robust quantitative brain mapping and ~9% TR increase. The motion-mitigated quantitative maps show significantly reduced artifacts and provide improved agreement with the maps obtained from motion-free scans.

Impact: We presented a motion-robust 3D multiparametric brain mapping technique that requires no external hardware for motion tracking and minimal increase (~9%) of the overall acquisition time.

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