Blind Analysis of CT Image Noise Using Residual Denoised Images

TL;DR

This paper introduces blind estimation techniques for assessing noise and signal quality in chest CT images, aiding protocol optimization and quality control through novel metrics and evaluation of filtering methods.

Contribution

It proposes new performance metrics for noise estimation accuracy and evaluates six filtering algorithms for their effectiveness in estimating relative noise levels in CT images.

Findings

Anisotropic diffusion and Wavelet-transform filters provide optimal noise estimates.

Metrics can evaluate filter parameter tradeoffs and noise trends.

Methods underestimate noise at low-flux levels.

Abstract

CT protocol design and quality control would benefit from automated tools to estimate the quality of generated CT images. These tools could be used to identify erroneous CT acquisitions or refine protocols to achieve certain signal to noise characteristics. This paper investigates blind estimation methods to determine global signal strength and noise levels in chest CT images. Methods: We propose novel performance metrics corresponding to the accuracy of noise and signal estimation. We implement and evaluate the noise estimation performance of six spatial- and frequency- based methods, derived from conventional image filtering algorithms. Algorithms were tested on patient data sets from whole-body repeat CT acquisitions performed with a higher and lower dose technique over the same scan region. Results: The proposed performance metrics can evaluate the relative tradeoff of filter parameters and noise estimation performance. The proposed automated methods tend to underestimate CT image noise at low-flux levels. Initial application of methodology suggests that anisotropic diffusion and Wavelet-transform based filters provide optimal estimates of noise. Furthermore, methodology does not provide accurate estimates of absolute noise levels, but can provide estimates of relative change and/or trends in noise levels.