Automated Selection of Uniform Regions for CT Image Quality Detection

TL;DR

This paper introduces a Fourier-transform based metric for assessing CT image quality by analyzing local spectral differences to identify regions with minimal noise, demonstrating high correlation with actual image noise levels.

Contribution

The study presents a novel Fourier-based metric that effectively estimates CT image noise by analyzing local spectral variations, outperforming traditional variance measures.

Findings

The proposed metric correlates strongly with CT image noise ($r=0.96$) in phantom images.

In combined phantom and patient images, the metric maintains high correlation ($r=0.95$).

Variance and standard deviation are less effective than the new metric for noise estimation.

Abstract

CT images are widely used in pathology detection and follow-up treatment procedures. Accurate identification of pathological features requires diagnostic quality CT images with minimal noise and artifact variation. In this work, a novel Fourier-transform based metric for image quality (IQ) estimation is presented that correlates to additive CT image noise. In the proposed method, two windowed CT image subset regions are analyzed together to identify the extent of variation in the corresponding Fourier-domain spectrum. The two square windows are chosen such that their center pixels coincide and one window is a subset of the other. The Fourier-domain spectral difference between these two sub-sampled windows is then used to isolate spatial regions-of-interest (ROI) with low signal variation (ROI-LV) and high signal variation (ROI-HV), respectively. Finally, the spatial variance ($var$), standard deviation ($std$), coefficient of variance ($cov$) and the fraction of abdominal ROI pixels in ROI-LV ($\nu'(q)$), are analyzed with respect to CT image noise. For the phantom CT images, $var$ and $std$ correlate to CT image noise ($|r|>0.76$ ($p\ll0.001$)), though not as well as $\nu'(q)$ ($r=0.96$ ($p\ll0.001$)). However, for the combined phantom and patient CT images, $var$ and $std$ do not correlate well with CT image noise ($|r|<0.46$ ($p\ll0.001$)) as compared to $\nu'(q)$ ($r=0.95$ ($p\ll0.001$)). Thus, the proposed method and the metric, $\nu'(q)$, can be useful to quantitatively estimate CT image noise.