Diffusion tensor imaging (DTI) with retrospective motion correction for large-scale pediatric imaging

J Magn Reson Imaging. 2012 Oct;36(4):961-71. doi: 10.1002/jmri.23710. Epub 2012 Jun 11.

Abstract

Purpose: To develop and implement a clinical DTI technique suitable for the pediatric setting that retrospectively corrects for large motion without the need for rescanning and/or reacquisition strategies, and to deliver high-quality DTI images (both in the presence and absence of large motion) using procedures that reduce image noise and artifacts.

Materials and methods: We implemented an in-house built generalized autocalibrating partially parallel acquisitions (GRAPPA)-accelerated diffusion tensor (DT) echo-planar imaging (EPI) sequence at 1.5T and 3T on 1600 patients between 1 month and 18 years old. To reconstruct the data, we developed a fully automated tailored reconstruction software that selects the best GRAPPA and ghost calibration weights; does 3D rigid-body realignment with importance weighting; and employs phase correction and complex averaging to lower Rician noise and reduce phase artifacts. For select cases we investigated the use of an additional volume rejection criterion and b-matrix correction for large motion.

Results: The DTI image reconstruction procedures developed here were extremely robust in correcting for motion, failing on only three subjects, while providing the radiologists high-quality data for routine evaluation.

Conclusion: This work suggests that, apart from the rare instance of continuous motion throughout the scan, high-quality DTI brain data can be acquired using our proposed integrated sequence and reconstruction that uses a retrospective approach to motion correction. In addition, we demonstrate a substantial improvement in overall image quality by combining phase correction with complex averaging, which reduces the Rician noise that biases noisy data.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Algorithms*
  • Artifacts*
  • Brain / anatomy & histology*
  • Child
  • Child, Preschool
  • Diffusion Magnetic Resonance Imaging / methods*
  • Humans
  • Image Enhancement / methods*
  • Image Interpretation, Computer-Assisted / methods*
  • Infant
  • Infant, Newborn
  • Male
  • Models, Biological
  • Motion
  • Movement
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Retrospective Studies
  • Sensitivity and Specificity