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Magn Reson Med. 2018 Mar;79(3):1595-1601. doi: 10.1002/mrm.26776. Epub 2017 Jun 15.

Gradient nonlinearity effects on upper cervical spinal cord area measurement from 3D T1 -weighted brain MRI acquisitions.

Author information

Department of Neurology, University of California San Francisco, San Francisco, California, USA.
Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Department of Neurology, Johns Hopkins University, Baltimore, Maryland, USA.
Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
Yale University, School of Medicine, New Haven, Connecticut, USA.
Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA.
Department of Neurology, University of Toronto, Toronto, Canada.
Department of Neurology, University of Southern California, Los Angeles, California, USA.
Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, Bethesda, Maryland.
Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA.
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Department of Radiology, University of California San Francisco, San Francisco, California, USA.
A complete list of the NAIMS participants is provided in the Acknowledgments section.



To explore (i) the variability of upper cervical cord area (UCCA) measurements from volumetric brain 3D T1 -weighted scans related to gradient nonlinearity (GNL) and subject positioning; (ii) the effect of vendor-implemented GNL corrections; and (iii) easily applicable methods that can be used to retrospectively correct data.


A multiple sclerosis patient was scanned at seven sites using 3T MRI scanners with the same 3D T1 -weighted protocol without GNL-distortion correction. Two healthy subjects and a phantom were additionally scanned at a single site with varying table positions. The 2D and 3D vendor-implemented GNL-correction algorithms and retrospective methods based on (i) phantom data fit, (ii) normalization with C2 vertebral body diameters, and (iii) the Jacobian determinant of nonlinear registrations to a template were tested.


Depending on the positioning of the subject, GNL introduced up to 15% variability in UCCA measurements from volumetric brain T1 -weighted scans when no distortion corrections were used. The 3D vendor-implemented correction methods and the three proposed methods reduced this variability to less than 3%.


Our results raise awareness of the significant impact that GNL can have on quantitative UCCA studies, and point the way to prospectively and retrospectively managing GNL distortions in a variety of settings, including clinical environments. Magn Reson Med 79:1595-1601, 2018. © 2017 International Society for Magnetic Resonance in Medicine.


3D T1-weighted brain MRI acquisitions; correction algorithms; gradient nonlinearity; spinal cord atrophy; upper cervical spinal cord area

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