Variability in image quality and radiation dose within and across 97 medical facilities

Abstract

Purpose: To characterize variability in image quality and radiation dose across a large cohort of computed tomography (CT) examinations and identify the scan factors with the highest influence on the observed variabilities. Approach: This retrospective institutional-review-board-exempt investigation was performed on 87,629 chest and abdomen-pelvis CT scans acquired for 97 facilities from 2018 to 2019. Images were assessed in terms of noise, resolution, and dose metrics (global noise, frequency in which modulation transfer function is at 0.50, and volumetric CT dose index, respectively). The results were fit to linear mixed-effects models to quantify the variabilities as affected by scan parameters and settings and patient characteristics. A list of factors, ranked by $t$ -value with $p<0.05$ , was ascertained for each of the six mixed effects models. A type III $p$ -value test was used to assess the influence of facility. Results: Across different facilities, image quality and dose were significantly different ( $p<0.05$ ), with little correlation between their mean magnitudes and consistency (Pearson's correlation $\text{coefficient}<0.34$ ). Scanner model, slice thickness, recon field-of-view and kernel, mAs, kVp, patient size, and centering were the most influential factors. The two body regions exhibited similar rankings of these factors for noise (Spearman's correlation $\text{coefficient}=0.76$ ) and dose (Spearman's correlation $\text{coefficient}=0.86$ ) but not for resolution (Spearman's correlation $\text{coefficient}=0.52$ ). Conclusions: Clinical CT scans can vary in image quality and dose with broad implications for diagnostic utility and radiation burden. Average scan quality was not correlated with interpatient scan-quality consistency. For a given facility, this variability can be quite large, with magnitude differences across facilities. The knowledge of the most influential factors per body region may be used to better manage these variabilities within and across facilities.

Keywords: Image quality; computed tomography; patient-specific; radiation dose.

Fig. 1

Four slices from different CT scans show how noise and resolution differ between different scans—with differences in (a) image resolution and (b) image noise showcased. In the chest exams (a), the sharpness difference is evident in the depiction of vessels. In the abdomen-pelvis exams (b), the difference is evident in the noisiness of the patients’ livers.

Fig. 2

Relationships between image quality and dose at each facility (differentiated by color) for (a) abdomen-pelvic exams and (b) chest exams. (c) A carve-out of the regions of three sample facilities for the noise versus dose plot [(b), column 2). In all columns and (a)–(c), each color area represents the range of likely values for a facility via Gaussian KDE. Displayed are the contour areas of 50% of max of the KDE (this used to estimate probability density function) regions for noise, resolution, or dose. Variability in the imaging enterprise shows itself in two ways: (1) interfacility variability is demonstrated by the different $x-y$ locations of each of the colored regions and (2) higher intrafacility variability is demonstrated by those regions that encompass a greater area (and thus less consistent image quality/dose values). Each of three example facilities in (c) exhibits a different amount of intrafacility variability in image quality and dose (denoted by their areas), as well as interfacility variability between them (denoted by their locations).

Fig. 3

(a) The best linear unbiased parameter estimates of the facility ID term for random effects model A reflect both high levels of interfacility variability and intrafacility variability (shown in the whisker plots). (b) However, there is little correlation between noise/resolution/dose values at a facility and variability at that facility. The blue points show the most likely value of noise, resolution, and dose at each facility for abdomen-pelvis exams. Thus differences in the location of these points reflect interfacility variability in image quality and dose. The error bars denote the 95% confidence interval of the estimate and reflect intrafacility variability at each facility in the same quantities—i.e., shorter bars at a facility denote more consistency and less variability within the scan population at that facility. (b) shows these estimate values for each facility against the uncertainty in the estimates with associated correlation coefficients between the two values. The low correlation values show that there is little relation between the most likely image quality/dose level at a facility and the variability in quality/dose across patients at that facility. That is, consistency and magnitude of noise/resolution/dose level are not well-correlated.

Fig. 4

A summary of the fit results of a mixed effects model B for abdomen-pelvis and chest exams. This figure shows which fixed effects were most explanatory for resolution, noise, and dose (with $p<0.05$). Colors denote the magnitude of an effect (using log scale). Black cells denote the effects that were not significant. For discrete features with sublevels, sublevel with largest magnitude is shown.

Fig. 5

Random effects from mixed effects model B indicating the contribution of leading protocol factors to the total variability in noise/resolution/dose between scans. Random effects with greater contribution are denoted by a darker green. The residual terms account for the fixed effects as well as the estimation error of the models. Table A details how much variance is explained by each random effect. Table B shows the number of facilities with a significant $p$-value for the indicated protocol factor. A darker green denotes which effects were more commonly significant at facilities.