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The Noise Power Spectra (NPS) only characterizes first and second order statistics associated with noise in Computed
Tomography (CT) reconstructions. The purpose of this work is to characterize the impact of the higher order statistics on
perception of noise texture for a variety of reconstruction algorithms. Images of a 32 cm water phantom were acquired on
the Aquilion ONE Genesis CT system and reconstructed with AiCE deep learning reconstruction (DLR), model-based
iterative reconstruction (MBIR), hybrid iterative reconstruction (AIDR), and filtered backprojection (FBP). Regions of
interest (ROIs) of 100x100pixels were extracted from the center of the images and 4th order statistics of each ROI were
assessed via excess kurtosis measurement. Pure Gaussian noise counterpart image datasets with the same mean, standard
deviation (SD), and NPS as each acquired data condition were also generated by convolving random white noise with the
root-NPS of the acquired data. Nine naïve observers were tasked with distinguishing the acquired noise image from the
pure Gaussian counterpart via a two-alternative forced choice experiment. Excess kurtosis in the image ROIs was 0.01
for FBP, 0.74 to 0.85 for FIRST, 0.03 to 0.08 for AIDR, and -0.13 to 0.21 for AiCE. Results showed the FBP images appeared
indistinguishable from their pure Gaussian counterparts with a Percent Correct (PC)=54%, while MBIR images were
readily distinguishable from their pure Gaussian counterparts, PC=98 to 100%. DLR and AIDR images are more difficult
to distinguish from their pure Gaussian counterparts, with the PC ranging from 58% to 88%. The discriminability index
derived from the PCs correlated strongly with excess kurtosis.
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