Dataset related to article "Quantifying inter-phantom variability in digital mammography: Implications for quality control"

The dataset for this study contains measurements of image quality metrics across 22 mammographic phantoms (Leeds TORMAS) and 10 repeated images each. It is structured with the following columns:

1. Metric: This column identifies the specific image quality (IQ) metric being measured. Examples include contrast-to-noise ratio (CNR) for specific details, the area under the modulation transfer function (AUMTF), and spatial frequencies corresponding to different modulation percentages.
2. PhantomID: A unique identifier for each phantom used in the study, allowing differentiation and tracking of data from the 22 original phantoms. 
3. Image#: An identifier for individual images acquired during repeated measurements of the same phantom. Ten images were obtained per phantom to account for intra-phantom variability.
4. Value: The measured value of the specified IQ metric for the given phantom and image. This represents the raw data used for subsequent statistical analysis of intra- and inter-phantom variability.

This dataset enables analysis of variability within phantoms (intra-phantom) and between phantoms (inter-phantom) for each metric, contributing to the evaluation of phantom consistency and reliability in image quality assessment.