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Physicists generally use the Noise Power Spectra (NPS) and Standard Deviation (SD) to characterize noise properties associated with non-linear reconstruction algorithms. However, these metrics capture only first and second order statistics. The purpose of this work is to characterize the impact of the higher order statistics, commonly associated with non-linear reconstruction, on noise texture. Images of a 32cm water phantom were acquired on the Aquilion ONE Genesis Computed Tomography (CT) system and reconstructed with 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. Pure Gaussian noise counterpart image datasets with the same mean, 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 its pure Gaussian counterpart via a two-alternative forced choice experiment. Results showed the FBP images appeared indistinguishable from their pure Gaussian counterparts (Percent Correct=54%), while MBIR images were readily distinguishable from Gaussian ones (Percent Correct=98-100%). DLR and AIDR images were more difficult to distinguish from their pure Gaussian counterparts (Percent Correct=58-88%), than MBIR, which indicates that it is more similar in perceived texture to Gaussian noise. This work demonstrates the appearance of CT noise texture may be dependent on higher orders statistics not captured by the NPS; noise textures with identical NPS and SD can be distinguished based on non-Gaussian properties.
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