Inter-phantom variability in digital mammography: implications for quality control

Phantoms play a critical role in mammography quality control (QC) by providing standardized conditions for evaluating image quality (IQ) metrics. However, inter-phantom variability may affect the reliability of these metrics, especially for inter-system comparisons. The aim of this study was to quantify the intra- and inter-phantom variability of IQ metrics using a set of theoretically identical phantoms.

Twenty-four TORMAS phantoms were imaged ten times each using a mammography unit under standardized high-dose conditions. Images were analyzed using automated software to extract 64 IQ metrics, including contrast-to-noise ratio (CNR) as well as modulation transfer function (MTF)-related and other metrics. Outliers were identified and excluded. Variability was assessed by calculating intra- and inter-phantom variances and coefficients of variation (COVs). The relative contributions of intra- and inter-phantom variability to total variability were also determined.

Two defective phantoms were excluded. Analysis of 64 IQ metrics across 22 phantoms showed higher inter-phantom variability compared to intra-phantom variability. Mean intra- and inter-phantom COVs were 6.9% and 15.1% for the 34 CNR metrics, 4.8% and 5.4% for the 5 MTF-related metrics, 0.14% and 0.75% for the 10 contrast metrics, 4.9% and 14.8% for the 15 noise metrics, respectively. Inter-phantom variability contributed 84.2% to total variability, highlighting its dominance.

Inter-phantom variability significantly affects IQ metrics, emphasizing the importance of using the same phantom for inter-system comparisons to avoid confounding results. Conversely, phantoms are well-suited for assessing system reproducibility over time, focus on inter-system variability while consistently using a single phantom.

This study highlights the significant impact of inter-phantom variability on image quality assessment, emphasizing the importance of using the same phantom for benchmarking imaging systems. These findings are crucial for optimizing quality control protocols and ensuring reliable, reproducible evaluations.

Inter-phantom variability exceeded intra-phantom variability across all image quality metrics of digital mammography. Subtle details showed higher total variability compared to more distinct features. Modulation transfer function metrics exhibited comparable intra- and inter-phantom variability, highlighting positioning sensitivity. Inter-phantom variability contributes 84% to total variability, impacting imaging system comparisons. Using the same phantom ensures reliability in imaging system performance evaluations.