The objectives of this study are as follows: to describe practical implementation challenges of multisite, multivendor quantitative studies; to describe the MRI phantom and analysissoftware used in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, illustrate the utility of the system for measuring scanner performance, the ability to assess gradient field nonlinearity corrections: and to recover human brainimages without geometric scaling errors in multisite studies. ADNI is a large multicenter study with each center having its own copy of the phantom. The design of the phantom and analysissoftware are presented as results from predistribution systematics studies and results from field experience with the phantom at 58 enrolling ADNI sites over a period. The estimated coefficients of variation intrinsic to measurements of geometry in a single phantom are in the range of 3–5 parts in . Phantom measurements accurately detect linear and nonlinear scaling in images. Gradient unwarping methods are readily assessed by phantom nonlinearity measurements. Phantom-based scaling correction reduces observed geometric drift in human images by one-third or more. Repair or replacement of phantoms between scans, however, is a confounding factor. The ADNI phantom can be used to assess both scanner performance and the validity of postprocessing image corrections in order to reduce systematic errors in human images. Reduced measurement errors should decrease measurement bias and increase statistical power for measurements of rates of change in the brain structure in AD treatment trials. Perhaps the greatest practical value of incorporating ADNI phantom measurements in a multisite study is to identify scanner errors through central monitoring. This approach has resulted in identification of system errors including sites misidentification of their own gradient hardware and the disabling of autoshim, and a miscalibrated laser alignment light. If undetected, these errors would have contributed to imprecision in quantitative metrics at over 25% of all enrolling ADNI sites.
This project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI; Principal Investigator: Michael Weiner; NIH Grant No. U01 AG024904). ADNI is funded by the National Institute of Aging, the National Institute of Biomedical Imaging and Bioengineering (NIBIB), and the Foundation for the National Institutes of Health, through generous contributions from the following companies and organizations: Pfizer Inc., Wyeth Research, Bristol-Myers Squibb, Eli Lilly and Company, Glaxo-SmithKline, Merck and Co. Inc., AstraZeneca AB, Novartis Pharmaceuticals Corporation, the Alzheimer’s Association, Eisai Global Clinical Development, Elan Corporation plc, Forest Laboratories, and the Institute for the Study of Aging (ISOA), with participation from the U.S. Food and Drug Administration. Support was also through National Institute of Aging Grant No. R01 AG11378. Additional infrastructure support was funded through NIH Grant No. C06 RR018898 and AG11378. Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). A complete listing of ADNI investigators who contributed to ADNI design, implementation and data collection is available at http://www.loni.ucla.edu/ADNI/Collaboration/ADNI̱Authorship̱list.pdf).
II. MATERIALS AND METHODS
II.A. Description of ADNI MR image data and study design
II.B. Phantom design and analysis
II.B.1. Phantom design
II.B.3. Pattern recognition and sphere finding
II.B.4. SNR sphere location and analysis
II.B.5. Fiducial sphere finding and analysis
II.B.6. CNR sphere analysis
II.B.7. Extracting MRI system geometric information
II.B.8. Linear (scaling) and nonlinearity measures
II.C. Studies prior to phantom distribution (systematics)
II.D. Longitudinal measures of scaling in ADNI scanners
II.E. Use of phantom measurements to correct linear scaling changes in human images
III. RESULTS AND DISCUSSIONS
III.A. Qualitative evaluation of geometric performance
III.B. Calibration exercise
III.C. Stability of serial measurements with a single “master” phantom
III.D. Measurement uncertainty and construction variability across the ADNI phantom fleet
III.E. Nonlinearity estimates
III.F. Longitudinal tracking of individual scanners with phantom measurements
III.G. Use of phantom measurements to correct within-scanner linear scaling changes in human images
III.H. Use of phantom measurements to perform absolute scaling of human images across scanners
III.I. Verifying the correctness of gradient warping corrections
III.J. Detecting system errors with the ADNI phantom
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