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. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|