%0 Journal Article
%T Effective x-ray attenuation coefficient measurements from two full field digital mammography systems for data calibration applications
%A John J Heine
%A Jerry A Thomas
%J BioMedical Engineering OnLine
%D 2008
%I BioMed Central
%R 10.1186/1475-925x-7-13
%X Logarithmic response calibration curves and effective x-ray attenuation coefficients were measured from two full field digital mammography (FFDM) systems with breast tissue equivalent phantom imaging and compared. Normalization methods were studied to assess the possibility of reducing the amount of calibration data collection. The percent glandular calibration map functional form was investigated. Spatial variations in the calibration data were used to assess the uncertainty in the calibration application by applying error propagation analyses.Logarithmic response curves are well approximated as linear. Measured effective x-ray attenuation coefficients are characteristic quantities independent of the imaging system and are in agreement with those predicted numerically. Calibration data collection can be reduced by applying a simple normalization technique. The calibration map is well approximated as linear. Intrasystem calibration variation was on the order of four percent, which was approximately half of the intersystem variation.FFDM systems provide a quantitative output, and the calibration quantities presented here may be used for data acquired on similar FFDM systems.Early detection is a key element in reducing breast cancer mortality [1]. Mammography screening is an essential surveillance component for early detection [2]. Similarly, there is interest in developing total cancer care methods in clinical practice so that disease screening and treatment can be tailored to the patient [3]. The development of accurate breast cancer risk models may play an important role in designing risk based cancer control strategies. Because breast density is a significant breast cancer risk factor [4], it may be useful to include it in the clinical setting for risk assessment. The Gail breast cancer risk model is used for intervention studies and counseling [5] but does not include breast density beyond research purposes. There is a critical need to incorporate all available i
%U http://www.biomedical-engineering-online.com/content/7/1/13