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Magn Reson Med. 2017 Jun 15. doi: 10.1002/mrm.26776. [Epub ahead of print]

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

Author information

1
Department of Neurology, University of California San Francisco, San Francisco, California, USA.
2
Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
3
Department of Neurology, Johns Hopkins University, Baltimore, Maryland, USA.
4
Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
5
Yale University, School of Medicine, New Haven, Connecticut, USA.
6
Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA.
7
Department of Neurology, University of Toronto, Toronto, Canada.
8
Department of Neurology, University of Southern California, Los Angeles, California, USA.
9
Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, Bethesda, Maryland.
10
Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA.
11
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
12
Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA.
13
Department of Radiology, University of California San Francisco, San Francisco, California, USA.
14
A complete list of the NAIMS participants is provided in the Acknowledgments section.

Abstract

PURPOSE:

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.

METHODS:

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.

RESULTS:

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%.

CONCLUSIONS:

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, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

KEYWORDS:

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

PMID:
28617996
DOI:
10.1002/mrm.26776
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