Generating big-data is becoming imperative with the advent of machine learning. Neuroimaging networks respond to this need. Italian Research Neurological Institutes have formed an advanced neuroimaging network to develop protocols for multisite studies. The present work reports on ACR phantom data across sites and evaluates accuracy and longitudinal reproducibility of: uniformity and ghosting, geometric accuracy, slice thickness, high-contrast and low-contrast object detectability. Our findings show that uniformity, geometric accuracy, low-contrast object detectability are measures that failed at some sites. We intervened to correct these issues improving protocol quality and scanner stability, establishing levels of precision relevant for future multicentre studies in quantitative imaging.