Marieke van Rosmalen

Quantitative MRI characteristics of chronic inflammatory neuropathies 97 6 Figure 6.1 Overview of processing pipeline Adiffusion-weighted image and a resampled 3D TSE SPIR are obtained (A, upper and lower image respectively). After manually drawn masks of the brachial plexus area (B) the automatic processing pipeline results in whole volume fiber tractography (C). Nerve locations are found in a tract density map (D) which specifies region of interests (E). A connectivity analysis results in reconstruction of nerve roots (F) and subsequently in nerve root segments from which diffusion parameters are derived (G). Statistical analysis For statistical analysis we used IBM SPSS Statistics (Version 25, Armonk, NewYork, United States). To compare patient characteristics, we used one-way analysis of variance (ANOVA) for numerical data and a Chi-squared test for categorical data. We compared diffusion parameters, T2 relaxation times, and fat fraction per side (i.e. right/left) using a paired sample t test and corrected for multiple testing using the Bonferroni method. To analyze diffusion parameters, T2 relaxation times, and fat fraction between groups we used an univariate general linear model with the MRI parameters as the dependent variable and the study group as a fixed factor. Tukey HSD was used to correct for multiple testing. A p value < 0.05 was considered significant. We analyzed diffusion parameters, T2 relaxation times, and fat fraction of all nerve roots together and per nerve root (i.e. C5, C6, C7) separately. Correlations between the quantitative parameters and clinical data were analyzed using the Pearson correlation coefficient r . We considered r ≤ 0.35 as a weak correlation, 0.36 – 0.70 as moderate, 0.70 – 0.89 as high and ≥ 0.90 as a very high correlation. 31