Retrospective rigid motion correction of three-dimensional magnetic resonance fingerprinting of the human brain

Abstract

Purpose: To obtain three-dimensional (3D), quantitative and motion-robust imaging with magnetic resonance fingerprinting (MRF).

Methods: Our acquisition is based on a 3D spiral projection k-space scheme. We compared different orderings of trajectory interleaves in terms of rigid motion-correction robustness. In all tested orderings, we considered the whole dataset as a sum of 56 segments of 7-s duration, acquired sequentially with the same flip angle schedule. We performed a separate image reconstruction for each segment, producing whole-brain navigators that were aligned to the first segment using normalized correlation. The estimated rigid motion was used to correct the k-space data, and the aligned data were matched with the dictionary to obtain motion-corrected maps.

Results: A significant improvement on the motion-affected maps after motion correction is evident with the suppression of motion artifacts. Correlation with the motionless baseline improved by 20% on average for both T₁ and T₂ estimations after motion correction. In addition, the average motion-induced quantification bias of 70 ms for T₁ and 18 ms for T₂ values was reduced to 12 ms and 6 ms, respectively, improving the reliability of quantitative estimations.

Conclusion: We established a method that allows correcting 3D rigid motion on a 7-s timescale during the reconstruction of MRF data using self-navigators, improving the image quality and the quantification robustness.

Keywords: 3D motion correction; MR fingerprinting.