An alternative method for noise analysis using pixel variance as part of quality control procedures on digital mammography systems.
According to the European Guidelines for quality assured breast cancer screening and diagnosis, noise analysis is one of the measurements that needs to be performed as part of quality control procedures on digital mammography systems. However, the method recommended in the European Guidelines does not discriminate sufficiently between systems with and without additional noise besides quantum noise. This paper attempts to give an alternative and relatively simple method for noise analysis which can divide noise into electronic noise, structured noise and quantum noise. Quantum noise needs to be the dominant noise source in clinical images for optimal performance of a digital mammography system, and therefore the amount of electronic and structured noise should be minimal. For several digital mammography systems, the noise was separated into components based on the measured pixel value, standard deviation (SD) of the image and the detector entrance dose. The results showed that differences between systems exist. Our findings confirm that the proposed method is able to discriminate systems based on their noise performance and is able to detect possible quality problems. Therefore, we suggest to replace the current method for noise analysis as described in the European Guidelines by the alternative method described in this paper.
National Expert and Training Centre, Radboud University Nijmegen Medical Centre (LRCB), Weg door Jonkerbos 90, 6532 SZ Nijmegen, The Netherlands. r.bouwman@lrcb.nl
R
R
Bouwman
K
K
Young
B
B
Lazzari
V
V
Ravaglia
M
M
Broeders
R
R
van Engen
D000465:Algorithms; D016477:Artifacts; D005060:Europe; D008327:Mammography / Q000592:standards*; D017410:Practice Guidelines as Topic; D011785:Quality Assurance, Health Care / Q000592:standards*; D011856:Radiographic Image Enhancement / Q000379:methods* / Q000592:standards*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity