22. Impact of iterative reconstructions on image quality in clinical CT images demonstrated by a novel noise power spectrum measurement tool

Abstract

Introduction To quantify noise texture and magnitude characteristics of clinical Computed Tomography (CT) images obtained with Iterative Reconstructions (IR) techniques. Methods An algorithm was developed to extract noise texture and magnitude metrics from 10 chest and abdomen clinical CT-scans (Discovery CT750 HD; GE Healthcare, Wisconsin) performed for oncology follow-up by measuring a global Noise Power Spectrum (NPS). After subtraction of adjacent slices, a segmentation algorithm was applied to exclude remaining anatomical structures. Noise magnitude (i.e. area under the 1D-NPS curve) and peak frequency (fpeak) were measured and compared for chest and abdomen on image subsets of all CT scans reconstructed using Filtered Back Projection (FBP), Adaptive Statistical Iterative Reconstruction 30% (ASIR30), 50% (ASIR50) and Model-Based Iterative Reconstruction (MBIR). Results Compared to FBP, ASIR30 and ASIR50, MBIR reduced noise magnitude by 31%, 17% and 13% in chest and by 47%, 34% and 29% in abdomen respectively ( p −1 respectively), ASIR50 (0.13 and 0.14 mm −1 respectively) and ASIR30 (0.16 and 0.18 mm −1 respectively) compared to FBP (0.23 and 0.27 mm −1 respectively; p Conclusions Assessing NPS of clinical CT examinations can demonstrate the reduction of noise magnitude and the changes of noise texture associated to IR. This method could be used to tailor CT protocols according to radiologists’ preferences regarding noise texture and magnitude.