On characterizing the sensitivity of lung computed tomography biomarkers to registration error

Abstract

Computed tomography (CT) scans have been widely used to evaluate lung health because of their ability to differentiate tissue densities. Recent studies such as COPDGene have collected inspiration and expiration CT scans from thousands of subjects, promising insight into the mechanical properties of lung tissue. These paired scans must be spatially aligned (i.e., registered) to extract biomarkers describing movement of lung tissue that may correlate with disease. Unfortunately, the relationship between registration and biomarker error is poorly characterized, a challenge to be addressed before registration-based biomarkers are used in clinical practice. In our analysis, we considered three registration-based biomarkers (Jacobian determinant, anisotropic deformation index, and slab-rod index) and demonstrated their sensitivity to modeled registration error. We provide a range of errors for a given biomarker, highlighting how both the magnitude of registration error and correlations between vectors in the registration error field influence biomarker error. We then describe a method to measure the error field for a particular registration algorithm and compare it with modeled registration error. These estimates enable the selection of an appropriate registration error model, which improve understanding of biomarker uncertainty. Quantifying the relationship between registration and biomarker error is crucial because it can inform registration algorithm selection to reduce error in future research studies, and in turn, result in robust imaging biomarkers for disease characterization.