title: Registration accuracy: how good is good enough? A statistical power calculation incorporating image registration uncertainty.
abstract: Image registration is an important tool for imaging validation studies investigating the effect of underlying focal disease on the imaging signal. The strength of the conclusions drawn from these analyses is limited by statistical power. Based on the observation that in this context, statistical power depends in part on uncertainty arising from registration error, we derive a power calculation formula relating registration error, sample size, and the minimum detectable difference between normal and pathologic regions on imaging. Statistical mappings between target registration error and fractional overlap metrics are also derived, and Monte Carlo simulations are used to evaluate the derived models and test the strength of their assumptions.
authors.affiliation: Robarts Research Institute, London, Canada.
authors.forename: Eli
authors.initials: E
authors.lastname: Gibson
authors.forename: Aaron
authors.initials: A
authors.lastname: Fenster
authors.forename: Aaron D
authors.initials: AD
authors.lastname: Ward
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique