Fractal dimension metric for quantifying noise texture of computed tomography images

Abstract

This study investigated a fractal dimension algorithm for noise texture quantification in CT images. Quantifying noise in CT images is important for assessing image quality. Noise is typically quantified by calculating noise standard deviation and noise power spectrum (NPS). Different reconstruction kernels and iterative reconstruction approaches affect both the noise magnitude and noise texture. The shape of the NPS can be used as a noise texture descriptor. However, the NPS requires numerous images for calculation and is a vector quantity. This study proposes the metric of fractal dimension to quantify noise texture, because fractal dimension is a single scalar metric calculated from a small number of images. Fractal dimension measures the complexity of a pattern. In this study, the ACR CT phantom was scanned and images were reconstructed using filtered back-projection with three reconstruction kernels: bone, soft and standard. Regions of interest were extracted from the uniform section of the phantom for NPS and fractal dimension calculation. The results demonstrated a mean fractal dimension of 1.86 for soft kernel, 1.92 for standard kernel, and 2.16 for bone kernel. Increasing fractal dimension corresponded to shift in the NPS towards higher spatial frequencies and grainier noise appearance. Stable fractal dimension was calculated from two ROI’s compared to more than 250 ROI’s used for NPS calculation. The scalar fractal dimension metric may be a useful noise texture descriptor for evaluating or optimizing reconstruction algorithms.