Table 1.

Sources of variability related to MRI scan acquisition and analysis and recommendation to reduce their impact for the most common imaging biomarkers used in MS.

Biomarkers	Sources of variability in MRI acquisition	Sources of variability in MRI analysis	Recommendations for harmonization of MRI acquisition and analysis
WM lesions	•Different acquisition protocols •Different magnetic field strengths •Different scanners •B0 or B1 inhomogeneity	•Inter rater variability in manual lesion segmentation •Variability in voxel intensity •Lack of deep learning generalizability •Registration pipelines (when used)	•Standardization of imaging acquisition protocols (i.e., use of isotropic 3D FLAIR with spatial resolution of 1 mm3 acquired at minimum 1.5 Tesla) •Inhomogeneity and intensity normalization •Careful QC before the analysis •Use of machine learning-based algorithms on images similar to those of the training dataset •Inclusion of magnetic field strength and image characteristics when merging lesions outputs from different scanners
Atrophy	•Different acquisition protocols •Different magnetic field strengths •Different scanners •B0 or B1 inhomogeneity	•Presence of black holes •Variability in voxel intensity •Defacing •Software variability •Lack of automated segmentation generalizability •Registration pipelines (when used)	•Standardization of imaging acquisition protocols and minimal hardware or software changes •Bias-field correction and intensity normalization •Careful QC before the analysis •Use of lesion-filled isotropic 3D T1-weighted images acquired at magnet iso-center
MTR-derived metrics	•Different acquisition protocols •Different magnetic field strengths •Different scanners •Strong dependency on radiofrequency pulse •Within scanner coil variability	•Registration pipeline (when used)	•Careful QC before the analysis •Use of constant parameters for acquisition, same transmission coil and correction for B1 errors •Careful check of ROI identification on MTR images registered from T1-weighted images
DTI-derived metrics	•Differences in vendors and magnetic field strengths •Different protocols (with B0 susceptibility distortions, number of diffusion gradients) •Eddy current distortions, Gibbs ringing artefacts, table vibration	•Software variability	•MRI acquisition with same magnetic field strength, same number of diffusion gradients, using parallel imaging and opposite phase-encoding directions •Same voxel size, B0 volumes, TE and TR •Careful QC before the analysis •Use of software tools to reduce the effects of table vibration •Denoising •Identical software setting for pre and post processing (correction for eddy currents, motion, and B0 and B1-inhomogeneity)
Functional MRI-derived metrics	•Different acquisition protocols •Different magnetic field strengths •Different scanners •B0 susceptibility distortions and B1 inhomogeneity •Eye movements artefacts, physiological noise artefacts, head motion	•Different preprocessing pipelines (head motion and physiological noise correction) and software •Method to quantify fMRI activation or functional connectivity	•MRI acquisition with same magnetic field strength, using opposite phase-encoding directions and same MRI protocol •Same temporal signal-to-fluctuation-noise-ratio across scanners •Careful QC before the analysis •EPI alignment and ICA analysis to correct for head motion •Scan to be performed with closed eyes to avoid/reduce eye movement artefacts. •Identical software setting for pre and post processing (motion, physiological noise correction and B0/B1-inhomogeneity)

Abbreviations: MR: Magnetic resonance; DL: Deep learning; QC: Quality Control; MTR: Magnetization Transfer Ratio; GM: Grey Matter; WM: White Matter; ROI: Region of Interest; TE: Echo Time; TR: Repetition Time; ICA: Independent Component Analysis; EPI: Echo planar imaging.