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Retrospective rigid motion correction of three‐dimensional magnetic resonance fingerprinting of the human brain
Author(s) -
Kurzawski Jan W.,
Cencini Matteo,
Peretti Luca,
Gómez Pedro A.,
Schulte Rolf F.,
Donatelli Graziella,
Cosottini Mirco,
Cecchi Paolo,
Costagli Mauro,
Retico Alessandra,
Tosetti Michela,
Buonincontri Guido
Publication year - 2020
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28301
Subject(s) - robustness (evolution) , artificial intelligence , computer science , magnetic resonance imaging , motion (physics) , computer vision , motion estimation , image quality , mathematics , image (mathematics) , chemistry , medicine , biochemistry , gene , radiology
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 T 1 and T 2 estimations after motion correction. In addition, the average motion‐induced quantification bias of 70 ms for T 1 and 18 ms for T 2 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.