The application of MRI requires high sensitivity of the MRI system to clinically relevant biological changes. However, MR images are susceptible to variability caused by other confounding factors. In addition to biological changes, variations in MR images may arise from participant-related physiological variability and device-related technical variability. These confounding factors can affect the observed relationship between the imaging data and the clinical or research outcome, potentially leading to biased or incorrect interpretations of the study results. Ensuring the reliability of MRI findings requires the identification and control of confounding factors. In a clinical setting, standard statistical approaches are not feasible for correcting confounding variabilities in MR images due to the limited sample size. Within our research group, an alternative method of clinical equivalence intervals was proposed to assess the impact of MRI-system upgrades on the resulting MR images. Our aim is to validate this novel technique for routine application in clinical practice.