High-resolution multishot spiral diffusion tensor imaging with inherent correction of motion-induced phase errors.

Published

Journal Article

PURPOSE: To develop and compare three novel reconstruction methods designed to inherently correct for motion-induced phase errors in multishot spiral diffusion tensor imaging without requiring a variable-density spiral trajectory or a navigator echo. THEORY AND METHODS: The first method simply averages magnitude images reconstructed with sensitivity encoding from each shot, whereas the second and third methods rely on sensitivity encoding to estimate the motion-induced phase error for each shot and subsequently use either a direct phase subtraction or an iterative conjugate gradient algorithm, respectively, to correct for the resulting artifacts. Numerical simulations and in vivo experiments on healthy volunteers were performed to assess the performance of these methods. RESULTS: The first two methods suffer from a low signal-to-noise ratio or from residual artifacts in the reconstructed diffusion-weighted images and fractional anisotropy maps. In contrast, the third method provides high-quality, high-resolution diffusion tensor imaging results, revealing fine anatomical details such as a radial diffusion anisotropy in cortical gray matter. CONCLUSION: The proposed sensitivity encoding + conjugate gradient method can inherently and effectively correct for phase errors, signal loss, and aliasing artifacts caused by both rigid and nonrigid motion in multishot spiral diffusion tensor imaging, without increasing the scan time or reducing the signal-to-noise ratio.

Full Text

Duke Authors

Cited Authors

  • Truong, T-K; Guidon, A

Published Date

  • February 2014

Published In

Volume / Issue

  • 71 / 2

Start / End Page

  • 790 - 796

PubMed ID

  • 23450457

Pubmed Central ID

  • 23450457

Electronic International Standard Serial Number (EISSN)

  • 1522-2594

Digital Object Identifier (DOI)

  • 10.1002/mrm.24709

Language

  • eng

Conference Location

  • United States