Abstract
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.
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