Abstract| Volume 16, ISSUE 4, SUPPLEMENT , S2-S3, July 2022

403 Comparison Of Iterative Reconstruction And Post Reconstruction Deep Learning Noise Reduction Methods Utilizing Philips Brilliance CT 256 Phantom Data And Clinical Images

      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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