Fast Variance Prediction for Iteratively Reconstructed CT Images With Locally Quadratic Regularization.

ABSTRACT: Predicting noise properties of iteratively reconstructed CT images is useful for analyzing reconstruction methods; for example, local noise power spectrum (NPS) predictions may be used to quantify the detectability of an image feature, to design regularization methods, or to determine dynamic tube current adjustment during a CT scan. This paper presents a method for fast prediction of reconstructed image variance and local NPS for statistical reconstruction methods using quadratic or locally quadratic regularization. Previous methods either require impractical computation times to generate an approximate map of the variance of each reconstructed voxel, or are restricted to specific CT geometries. Our method can produce a variance map of the entire image, for locally shift-invariant CT geom

SUBMITTER: Schmitt SM 

PROVIDER: S-EPMC5217761 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature