Inter-scanner brain MRI volumetric biases persist even in a harmonized multi-subject study of multiple sclerosis

Author:

Clark Kelly A.ORCID,O’Donnell Carly M.,Elliott Mark A.,Tauhid Shahamat,Dewey Blake E.ORCID,Chu Renxin,Khalil Samar,Nair GovindORCID,Sati Pascal,DuVal Anna,Pellegrini Nicole,Bar-Or AmitORCID,Markowitz Clyde,Schindler Matthew K.,Zurawski Jonathan,Calabresi Peter A.,Reich Daniel S.,Bakshi RohitORCID,Shinohara Russell T.,

Abstract

AbstractBackground/PurposeMulticenter 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 4 sites.Materials and MethodsImages 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 and T2 (FLAIR) lesions. Random effect and permutation-based nonparametric modeling was performed to assess differences in estimated volumes within and across sites.ResultsRandom effect modeling demonstrated model assumption violations for most comparisons of interest. Non-parametric 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 intra-scanner 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.ConclusionDifferences 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 inter-scanner differences show promise in reducing biases in multi-center studies of MS.

Publisher

Cold Spring Harbor Laboratory

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