Retrospective rigid motion correction of three-dimensional magnetic resonance fingerprinting of the human brain: Magnetic Resonance in Medicine

J.W. Kurzawski, M. Cencini, L. Peretti, P.A. Gómez, R.F. Schulte, G. Donatelli, M. Cosottini, P. Cecchi, M. Costagli, A. Retico, M. Tosetti, G. Buonincontri

Research output: Contribution to journalArticlepeer-review

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. © 2020 International Society for Magnetic Resonance in Medicine
Original languageEnglish
Pages (from-to)2606-2615
Number of pages10
JournalMagn. Reson. Med.
Volume84
Issue number5
DOIs
Publication statusPublished - 2020

Keywords

  • 3D motion correction
  • MR fingerprinting
  • Image enhancement
  • Image reconstruction
  • Image segmentation
  • Magnetic resonance
  • Magnetorheological fluids
  • 3D rigid motion
  • Motion artifact
  • Motion correction
  • Normalized correlation
  • Quantification bias
  • Quantitative estimation
  • Rigid motions
  • Threedimensional (3-d)
  • Motion estimation
  • artifact
  • brain
  • human
  • image quality
  • image reconstruction
  • motion
  • note
  • nuclear magnetic resonance
  • reliability
  • article

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