Literature DB >> 29887658

Phantom-based field maps for gradient nonlinearity correction in diffusion imaging.

Baxter P Rogers1,2,3,4, Justin Blaber5, Allen T Newton1,2, Colin B Hansen5, E Brian Welch1,2, Adam W Anderson2,4, Jeffrey J Luci6, Carlo Pierpaoli7, Bennett A Landman1,2,4,5.   

Abstract

Gradient coils in magnetic resonance imaging do not produce perfectly linear gradient fields. For diffusion imaging, the field nonlinearities cause the amplitude and direction of the applied diffusion gradients to vary over the field of view. This leads to site- and scan-specific systematic errors in estimated diffusion parameters such as diffusivity and anisotropy, reducing reliability especially in studies that take place over multiple sites. These errors can be substantially reduced if the actual scanner-specific gradient coil magnetic fields are known. The nonlinearity of the coil fields is measured by scanner manufacturers and used internally for geometric corrections, but obtaining and using the information for a specific scanner may be impractical for many sites that operate without special-purpose local engineering and research support. We have implemented an empirical field-mapping procedure using a large phantom combined with a solid harmonic approximation to the coil fields that is simple to perform and apply. Here we describe the accuracy and precision of the approach in reproducing manufacturer gold standard field maps and in reducing spatially varying errors in quantitative diffusion imaging for a specific scanner. Before correction, median B value error ranged from 33 - 41 relative to manufacturer specification at 100 mm from isocenter; correction reduced this to 0 - 4. On-axis spatial variation in the estimated mean diffusivity of an isotropic phantom was 2.2% - 4.1% within 60 mm of isocenter before correction, 0.5% - 1.6% after. Expected fractional anisotropy in the phantom was 0; highest estimated fractional anisotropy within 60 mm of isocenter was reduced from 0.024 to 0.012 in the phase encoding direction (48% reduction) and from 0.020 to 0.006 in the frequency encoding direction (72% reduction).

Entities:  

Keywords:  MRI; b-values; gradient field nonlinearity; quantitative diffusion imaging

Year:  2018        PMID: 29887658      PMCID: PMC5990280          DOI: 10.1117/12.2293786

Source DB:  PubMed          Journal:  Proc SPIE Int Soc Opt Eng        ISSN: 0277-786X


  15 in total

1.  How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging.

Authors:  Jesper L R Andersson; Stefan Skare; John Ashburner
Journal:  Neuroimage       Date:  2003-10       Impact factor: 6.556

2.  Practical estimate of gradient nonlinearity for implementation of apparent diffusion coefficient bias correction.

Authors:  Dariya I Malkyarenko; Thomas L Chenevert
Journal:  J Magn Reson Imaging       Date:  2014-12       Impact factor: 4.813

3.  Analysis and correction of gradient nonlinearity bias in apparent diffusion coefficient measurements.

Authors:  Dariya I Malyarenko; Brian D Ross; Thomas L Chenevert
Journal:  Magn Reson Med       Date:  2014-03       Impact factor: 4.668

4.  Stability of Gradient Field Corrections for Quantitative Diffusion MRI.

Authors:  Baxter P Rogers; Justin Blaber; E Brian Welch; Zhaohua Ding; Adam W Anderson; Bennett A Landman
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2017-03-09

5.  Improved correction for gradient nonlinearity effects in diffusion-weighted imaging.

Authors:  Ek T Tan; Luca Marinelli; Zachary W Slavens; Kevin F King; Christopher J Hardy
Journal:  J Magn Reson Imaging       Date:  2012-11-21       Impact factor: 4.813

6.  Gradient nonlinearity correction to improve apparent diffusion coefficient accuracy and standardization in the american college of radiology imaging network 6698 breast cancer trial.

Authors:  David C Newitt; Ek T Tan; Lisa J Wilmes; Thomas L Chenevert; John Kornak; Luca Marinelli; Nola Hylton
Journal:  J Magn Reson Imaging       Date:  2015-03-11       Impact factor: 4.813

7.  Demonstration of nonlinearity bias in the measurement of the apparent diffusion coefficient in multicenter trials.

Authors:  Dariya I Malyarenko; David Newitt; Lisa J Wilmes; Alina Tudorica; Karl G Helmer; Lori R Arlinghaus; Michael A Jacobs; Guido Jajamovich; Bachir Taouli; Thomas E Yankeelov; Wei Huang; Thomas L Chenevert
Journal:  Magn Reson Med       Date:  2015-05-02       Impact factor: 4.668

Review 8.  FSL.

Authors:  Mark Jenkinson; Christian F Beckmann; Timothy E J Behrens; Mark W Woolrich; Stephen M Smith
Journal:  Neuroimage       Date:  2011-09-16       Impact factor: 6.556

9.  An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging.

Authors:  Jesper L R Andersson; Stamatios N Sotiropoulos
Journal:  Neuroimage       Date:  2015-10-20       Impact factor: 6.556

10.  The minimal preprocessing pipelines for the Human Connectome Project.

Authors:  Matthew F Glasser; Stamatios N Sotiropoulos; J Anthony Wilson; Timothy S Coalson; Bruce Fischl; Jesper L Andersson; Junqian Xu; Saad Jbabdi; Matthew Webster; Jonathan R Polimeni; David C Van Essen; Mark Jenkinson
Journal:  Neuroimage       Date:  2013-05-11       Impact factor: 6.556

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  3 in total

1.  Consideration of Cerebrospinal Fluid Intensity Variation in Diffusion Weighted MRI.

Authors:  Colin B Hansen; Vishwesh Nath; Allison E Hainline; Kurt G Schilling; Prasanna Parvathaneni; Roza G Bayrak; Justin A Blaber; Owen Williams; Susan Resnick; Lori Beason-Held; Okan Irfanoglu; Carlo Pierpaoli; Adam W Anderson; Baxter P Rogers; Bennett A Landman
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2019-03

2.  Empirical field mapping for gradient nonlinearity correction of multi-site diffusion weighted MRI.

Authors:  Colin B Hansen; Baxter P Rogers; Kurt G Schilling; Vishwesh Nath; Justin A Blaber; Okan Irfanoglu; Alan Barnett; Carlo Pierpaoli; Adam W Anderson; Bennett A Landman
Journal:  Magn Reson Imaging       Date:  2020-11-19       Impact factor: 2.546

3.  The effect of gradient nonlinearities on fiber orientation estimates from spherical deconvolution of diffusion magnetic resonance imaging data.

Authors:  Fenghua Guo; Alberto de Luca; Greg Parker; Derek K Jones; Max A Viergever; Alexander Leemans; Chantal M W Tax
Journal:  Hum Brain Mapp       Date:  2020-10-09       Impact factor: 5.399

  3 in total

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