The Italian Neuroimaging Network Initiative (INNI) supports the creation of a repository, where MRI, clinical, and neuropsychological data from multiple sclerosis (MS) patients and healthy controls are collected from Italian Research Centers with internationally recognized expertise in MRI applied to MS. However, multicenter MRI data integration needs standardization and quality control (QC). This study aimed to implement quantitative measures for characterizing the standardization and quality of MRI collected within INNI. MRI scans of 423 MS patients, including 3D T1- and T2-weighted, were obtained from INNI repository (from Centers A, B, C, and D). QC measures were implemented to characterize: (1) head positioning relative to the magnet isocenter; (2) intensity inhomogeneity; (3) relative image contrast between brain tissues; and (4) image artefacts. Centers A and D showed the most accurate subject positioning within the MR scanner (median z-offsets = - 2.6 ± 1.7 cm and - 1.1 ± 2 cm). A low, but significantly different, intensity inhomogeneity on 3D T1-weighted MRI was found between all centers (p < 0.05), except for Centers A and C that showed comparable image bias fields. Center D showed the highest relative contrast between gray and normal appearing white matter (NAWM) on 3D T1-weighed MRI (0.63 ± 0.04), while Center B showed the highest relative contrast between NAWM and MS lesions on FLAIR (0.21 ± 0.06). Image artefacts were mainly due to brain movement (60%) and ghosting (35%). The implemented QC procedure ensured systematic data quality assessment within INNI, thus making available a huge amount of high-quality MRI to better investigate pathophysiological substrates and validate novel MRI biomarkers in MS.
Keywords: Big data; Italian Neuroimaging Network Initiative (INNI); Magnetic resonance imaging (MRI); Multiple sclerosis (MS).