title: Automated analysis of phantom images for the evaluation of long-term reproducibility in digital mammography.
abstract: The performance of an automatic software package was evaluated with phantom images acquired by a full-field digital mammography unit. After the validation, the software was used, together with a Leeds TORMAS test object, to model the image acquisition process. Process modelling results were used to evaluate the sensitivity of the method in detecting changes of exposure parameters from routine image quality measurements in digital mammography, which is the ultimate purpose of long-term reproducibility tests. Image quality indices measured by the software included the mean pixel value and standard deviation of circular details and surrounding background, contrast-to-noise ratio and relative contrast; detail counts were also collected. The validation procedure demonstrated that the software localizes the phantom details correctly and the difference between automatic and manual measurements was within few grey levels. Quantitative analysis showed sufficient sensitivity to relate fluctuations in exposure parameters (kV(p) or mAs) to variations in image quality indices. In comparison, detail counts were found less sensitive in detecting image quality changes, even when limitations due to observer subjectivity were overcome by automatic analysis. In conclusion, long-term reproducibility tests provided by the Leeds TORMAS phantom with quantitative analysis of multiple IQ indices have been demonstrated to be effective in predicting causes of deviation from standard operating conditions and can be used to monitor stability in full-field digital mammography.
authors.affiliation: Department of Oncological and Surgical Sciences, University of Padova, via Gattamelata 64, 35128 Padova, Italy. gisella.gennaro@pd.infn.it
authors.forename: G
authors.initials: G
authors.lastname: Gennaro
authors.forename: F
authors.initials: F
authors.lastname: Ferro
authors.forename: G
authors.initials: G
authors.lastname: Contento
authors.forename: F
authors.initials: F
authors.lastname: Fornasin
authors.forename: C
authors.initials: C
authors.lastname: di Maggio
mesh_terms: D001185:Artificial Intelligence; D008327:Mammography / Q000295:instrumentation* / Q000379:methods*; D019047:Phantoms, Imaging; D011785:Quality Assurance, Health Care / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000295:instrumentation / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity