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Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling
Authors:Thomas L. Chenevert  Dariya I. Malyarenko  David Newitt  Xin Li  Mohan Jayatilake  Alina Tudorica  Andriy Fedorov  Ron Kikinis  Tiffany Ting Liu  Mark Muzi  Matthew J. Oborski  Charles M. Laymon  Xia Li  Yankeelov Thomas  Kalpathy-Cramer Jayashree  James M. Mountz  Paul E. Kinahan  Daniel L. Rubin  Brian D. Ross
Affiliation:2. Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA;3. Oregon Health & Science University, Portland, OR;4. Brigham and Women''s Hospital and Harvard Medical School, Boston, MA;5. Stanford University, Stanford, CA;11. Institute of Imaging Science, Vanderbilt University, Nashville, TN;12. Massachusetts General Hospital, Boston, MA
Abstract:PURPOSE: To evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image scaling employed by magnetic resonance imaging manufacturers. METHODS: A series of gadoteridol-doped distilled water solutions (0%, 0.5%, 1%, and 2% volume concentrations) was prepared for manual substitution into one (of three) phantom compartments to create “variable signal,” whereas the other two compartments (containing mineral oil and 0.25% gadoteriol) were held unchanged. Pseudodynamic images were acquired over multiple series using four scanners such that the histogram of pixel intensities varied enough to provoke variable image scaling from series to series. Additional diffusion-weighted images were acquired of an ice-water phantom to generate scanner-specific apparent diffusion coefficient (ADC) maps. The resulting pseudodynamic images and ADC maps were analyzed by eight centers of the Quantitative Imaging Network using 16 different SW tools to measure compartment-specific region-of-interest intensity. RESULTS: Images generated by one of the scanners appeared to have additional intensity scaling that was not accounted for by the majority of tested quantitative image analysis SW tools. Incorrect image scaling leads to intensity measurement bias near 100%, compared to nonscaled images. CONCLUSION: Corrective actions for image scaling are suggested for manufacturers and quantitative imaging community.
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