Objective assessment of image registration results using statistical confidence intervals

Precision of medical image registration is very important in clinical diagnosis and treatment, which is usually assessed by visual inspection or by referring to other methods that require special expertise and extensive experience. In this study, the authors proposed a novel automatic approach based on statistical theory to estimate confidence intervals of the registration parameters and allow the precision of registration results to be objectively assessed. Under the assumption of local linearity, statistical confidence intervals of model fitting (regression) can be used to evaluate registration precision. Monte Carlo simulations using the Hoffman brain phantom with various amounts of displacement, noise and spatial filtering were conducted to evaluate the formula for estimating the confidence intervals in 2D image registrations. Monte Carlo simulation results are consistent with the calculated confidence intervals, and the agreement is applicable to different amounts of translation, angular rotation and spatial smoothing. The estimated parameter values fall within the predicted 90%, 95% and 99% confidence intervals with less than ±1% of errors. The present results indicate that the use of statistical confidence intervals can provide an objective assessment of individual image registration results