Introduction: The quality of coronary computed tomographic angiography (CCTA) post processing is
dependent on special noise reduction (NR) techniques, such as Iterative Reconstruction
(IR). While IR lowers noise amplitude, it also lowers central noise frequency (CNF),
distorting margins and adding a waxy quality to CCTA images. Fortunately, new deep
learning (DL) NR algorithms preserve CNF maintaining sharp image features without
distortion. The most versatile of these new NR techniques is applicable after filtered
back projection (FBP) and is thus vendor agnostic. In this presentation, we validate
the relative CNF stability of an FDA-approved DL algorithm (PixelShine, AlgoMedica
Inc) through Noise Power Spectrum (NPS) analysis, comparing its performance to that
of the IR NR methods of a major CT vendor. We also provide a gallery of clinical comparisons.
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© 2022 Published by Elsevier Inc.
