Motion-robust diffusion compartment imaging using simultaneous multi-slice acquisition

Magn Reson Med. 2019 May;81(5):3314-3329. doi: 10.1002/mrm.27613. Epub 2018 Nov 16.

Abstract

Purpose: To achieve motion-robust diffusion compartment imaging (DCI) in near continuously moving subjects based on simultaneous multi-slice, diffusion-weighted brain MRI.

Methods: Simultaneous multi-slice (SMS) acquisition enables fast and dense sampling of k- and q-space. We propose to achieve motion-robust DCI via slice-level motion correction by exploiting the rigid coupling between simultaneously acquired slices. This coupling provides 3D coverage of the anatomy that substantially constraints the slice-to-volume alignment problem. This is incorporated into an explicit model of motion dynamics that handles continuous and large subject motion in robust DCI reconstruction.

Results: We applied the proposed technique, called Motion Tracking based on Simultanous Multislice Registration (MT-SMR) to multi b-value SMS diffusion-weighted brain MRI of healthy volunteers and motion-corrupted scans of 20 pediatric subjects. Quantitative and qualitative evaluation based on fractional anisotropy in unidirectional fiber regions, and DCI in crossing-fiber regions show robust reconstruction in the presence of motion.

Conclusion: The proposed approach has the potential to extend routine use of SMS DCI in very challenging populations, such as young children, newborns, and non-cooperative patients.

Keywords: Diffusion-compartment imaging; diffusion-weighted MRI; image-based navigation; intra-volume motion; motion tracking; motion-robust; simultaneous multi-slice; slice registration.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Adolescent
  • Adult
  • Algorithms
  • Anisotropy
  • Brain / diagnostic imaging*
  • Child
  • Child, Preschool
  • Diffusion Magnetic Resonance Imaging*
  • Healthy Volunteers
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods*
  • Models, Statistical
  • Motion
  • Reproducibility of Results