Asymptotic Normality of Kernel Estimators for Images Observed under the Radon Transform in Fan Beam Design

摘要

We consider a nonparametric, two-dimensional regression model that describes observations of Radon transformed images, i.e., an inverse regression model. Reconstructions from deterministic fan beam design by a certain kind of kernel-type estimators are considered and their asymptotic properties are investigated. The problem discussed is related to medical imaging procedures such as computerized tomography (CT).