Intersite brain MRI volumetric biases persist even in a harmonized multisubject study of multiple sclerosis
Kelly A. Clark, Carly M. O'Donnell, Mark A. Elliott, Shahamat Tauhid, Blake E. Dewey, Renxin Chu, Samar Khalil, Govind Nair, Pascal Sati, Anna DuVal, Nicole Pellegrini, Amit Bar‐Or, Clyde Markowitz, Matthew K. Schindler, Jonathan Zurawski, Peter A. Calabresi, Daniel S. Reich, Rohit Bakshi, Russell T. Shinohara,- Neurology (clinical)
- Radiology, Nuclear Medicine and imaging
Abstract
Background and Purpose
Multicenter study designs involving a variety of MRI scanners have become increasingly common. However, these present the issue of biases in image‐based measures due to scanner or site differences. To assess these biases, we imaged 11 volunteers with multiple sclerosis (MS) with scan and rescan data at four sites.
Methods
Images were acquired on Siemens or Philips scanners at 3 Tesla. Automated white matter lesion detection and whole‐brain, gray and white matter, and thalamic volumetry were performed, as well as expert manual delineations of T1 magnetization‐prepared rapid acquisition gradient echo and T2 fluid‐attenuated inversion recovery lesions. Random‐effect and permutation‐based nonparametric modeling was performed to assess differences in estimated volumes within and across sites.
Results
Random‐effect modeling demonstrated model assumption violations for most comparisons of interest. Nonparametric modeling indicated that site explained >50% of the variation for most estimated volumes. This expanded to >75% when data from both Siemens and Philips scanners were included. Permutation tests revealed significant differences between average inter‐ and intrasite differences in most estimated brain volumes (P < .05). The automatic activation of spine coil elements during some acquisitions resulted in a shading artifact in these images. Permutation tests revealed significant differences between thalamic volume measurements from acquisitions with and without this artifact.
Conclusion
Differences in brain volumetry persisted across MR scanners despite protocol harmonization. These differences were not well explained by variance component modeling; however, statistical innovations for mitigating intersite differences show promise in reducing biases in multicenter studies of MS.