Reliability of Longitudinal Brain Volume Loss Measurements between 2 Sites in Patients with Multiple Sclerosis: Comparison of 7 Quantification Techniques

Abstract

BACKGROUND AND PURPOSE:Brain volume loss is currently a MR imaging marker of neurodegeneration in MS. Available quantification algorithms perform either direct (segmentation-based techniques) or indirect (registration-based techniques) measurements. Because there is no reference standard technique, the assessment of their accuracy and reliability remains a difficult goal. Therefore, the purpose of this work was to assess the robustness of 7 different postprocessing algorithms applied to images acquired from different MR imaging systems.

Cortical Thickness of Native Tibetans in the Qinghai-Tibetan Plateau

“…First, brain images were obtained by using 2 MR imaging scanners. However, several studies have confirmed that the MR imaging scanner field strength, manufacturer, machine upgrade, and pulse sequence 77 and the different postprocessing algorithms applied to the images acquired from different MR imaging systems 78 have little effect on the reliability of cortical thickness measurements. Second, the Tibetan subjects had moved to the lowlands for 1-15 days, which would have an influence on cerebral blood flow.…”

Reliable measurements of brain atrophy in individual patients with multiple sclerosis

“…Note that is has been demonstrated in the past that using cross‐sectional methods to measure atrophy results in much higher errors compared to longitudinal approaches (Durand‐Dubief et al., 2012; Nakamura et al., 2014; Smith et al., 2002). Our results confirm these findings.…”

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.…”