Fast Variance Prediction for Iteratively Reconstructed CT with Arbitrary Geometries
نویسندگان
چکیده
Fast variance prediction for iteratively reconstructed CT images is useful for the analysis of reconstruction algorithms and potentially for automatic tube current modulation. Prior methods are either computationally intractable or require impractical computation times to produce a map of the reconstructed image variance. In this paper we present the extension of prior work for fast variance prediction, which was specific to limited classes of CT geometries, to arbitrary CT geometries. We compare the results of our method to an empirical variance map produced from repeated axial CT scans of a chest phantom.
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