Retrospective rigid motion correction of three‐dimensional magnetic resonance fingerprinting of the human brain. Issue 5 (5th May 2020)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Magnetic resonance in medicine. Volume 84:Issue 5(2020)
• Page Start: 2606
• Page End: 2615
• Publication Date: 2020-05-05
• Subjects: 3D motion correction -- MR fingerprinting; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28301
• Languages: English
• ISSNs: 0740-3194

Abstract

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.