title: Validation of a digital mammographic unit model for an objective and highly automated clinical image quality assessment.
abstract: In mammography, image quality assessment has to be directly related to breast cancer indicator (e.g. microcalcifications) detectability. Recently, we proposed an X-ray source/digital detector (XRS/DD) model leading to such an assessment. This model simulates very realistic contrast-detail phantom (CDMAM) images leading to gold disc (representing microcalcifications) detectability thresholds that are very close to those of real images taken under the simulated acquisition conditions. The detection step was performed with a mathematical observer. The aim of this contribution is to include human observers into the disc detection process in real and virtual images to validate the simulation framework based on the XRS/DD model. Mathematical criteria (contrast-detail curves, image quality factor, etc.) are used to assess and to compare, from the statistical point of view, the cancer indicator detectability in real and virtual images. The quantitative results given in this paper show that the images simulated by the XRS/DD model are useful for image quality assessment in the case of all studied exposure conditions using either human or automated scoring. Also, this paper confirms that with the XRS/DD model the image quality assessment can be automated and the whole time of the procedure can be drastically reduced. Compared to standard quality assessment methods, the number of images to be acquired is divided by a factor of eight.
authors.affiliation: Université de Lorraine, CRAN, UMR 7039, 2 Avenue de la Forêt de Haye, 54516 Vandœuvre-Lès-Nancy, France.
authors.forename: Hector
authors.initials: H
authors.lastname: Perez-Ponce
authors.forename: Christian
authors.initials: C
authors.lastname: Daul
authors.forename: Didier
authors.initials: D
authors.lastname: Wolf
authors.forename: Alain
authors.initials: A
authors.lastname: Noel
mesh_terms: D001941:Breast Diseases / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D011785:Quality Assurance, Health Care / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity