Volumetric Analysis from a Harmonized Multisite Brain MRI Study of a Single Subject with Multiple Sclerosis

Abstract

Background and Objectives Magnetic resonance imaging (MRI) can be used to measure structural changes in the brain of people with multiple sclerosis (MS), and is essential for diagnosis, longitudinal monitoring, and therapy evaluation. The North American Imaging in Multiple Sclerosis Cooperative (NAIMS) steering committee developed a uniform high-resolution 3T MRI protocol relevant to the quantification of cerebral lesions and atrophy and implemented it at seven sites across the United States. To assess inter-site variability in scan data, a volunteer with relapsing-remitting MS was imaged with a scan-rescan at each site. Materials and Methods All imaging was acquired on Siemens scanners (4 Skyra, 2 TIM Trio, and 1 Verio). Expert segmentations were manually obtained for T1-hypointense and T2 (FLAIR)-hyperintense lesions. Several automated lesion detection and whole-brain, cortical, and deep gray matter volumetric pipelines were applied. Statistical analyses were conducted to assess variability across sites, as well as systematic biases in the volumetric measurements that were site-related. Results Systematic biases due to site differences in expert-traced lesion measurements were significant (p<0.01 for both T1 and T2 lesion volumes), with site explaining over 90% of the variation (range = 13.0–16.4ml in T1 and 15.9–20.1ml in T2) in lesion volumes. Site also explained more than 80% of the variation in most automated volumetric measurements. Output measures clustered according to scanner models, with similar results from the Skyra vs. the other two units. Conclusions Even in multi-center studies with consistent scanner field strength and manufacturer, after protocol harmonization, systematic differences can lead to severe biases in volumetric analyses.

An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions

“…This patient was imaged at seven sites in the United States as part of a pilot study for the North American Imaging in Multiple Sclerosis (NAIMS) Cooperative. He was characterized as having mild-to-moderate physical disability, which was stable between the first and last visits, and had no clinical relapses nor radiological changes during the course of the study 19 .…”

“…Details of the image acquisition have been previously published 19 and are briefly summarized in this section. Whole-brain 3D high-resolution FLAIR, T2, and T1-weighted volumes were acquired on seven 3T Siemens MRI scanners across the United States (4 Skyra, 2 Tim Trio, 1 Verio).…”

“…The second context represented a manually supplemented version of the count, where a mask of lesion tissue was provided by an expert rater 19 , and the count was obtained using the segmentation probability map within the manual lesion tissue mask. In this case, variation in the count arises solely due to changes in the Hessian structure of the segmentation mask and changes in the manual segmentation.…”

Whole‐brain atrophy assessed by proportional‐ versus registration‐based pipelines from 3TMRIin multiple sclerosis

“…MRI‐derived volumetrics are prone to deviations throughout the data pipeline, including at the acquisition stage (e.g., head motion, hardware nonuniformity including magnetic field strength, gradient distortions, and pulse sequence type and parameters; Chu, Hurwitz, Tauhid, & Bakshi, 2017; Papinutto et al., 2017; Sharma et al., 2004; Shinohara et al., 2017) and segmentation procedure (e.g., preprocessing steps—inhomogeneity correction, method of tissue class segmentation, and normalization; Chard, Parker, Griffin, Thompson, & Miller, 2002; Chu, Hurwitz, et al., 2017; Durand‐Dubief et al., 2012; Granberg et al., 2016; Kazemi & Noorizadeh, 2014; Popescu, Schoonheim, et al., 2016; Vidal‐Jordana et al., 2017). Furthermore, brain volume may vary based on pathophysiological factors, including recent start of immunomodulatory therapy, acute inflammation, hydration status, time of day, tobacco use, genetics, and comorbid conditions (Rocca et al., 2017).…”

“…Recent studies have examined the precision of metrics from 1.5T or 3T scanners using standardized acquisition parameters and software pipelines; all concluded that intrascanner variance was generally minimal, whereas interscanner variability was consistently a source of significant bias (Biberacher et al., 2016; Durand‐Dubief et al., 2012; Papinutto et al., 2017; Shinohara et al., 2017). The type of postprocessing software pipeline was also associated with divergent measurements in brain volumetrics in those studies.…”