The effect of breast compression on mass conspicuity in digital mammography.
This study analyzed how the inherent quality of diagnostic information in digital mammography could be affected by breast compression. A digital mammography system was modeled using a Monte Carlo algorithm based on the Penelope program, which has been successfully used to model several medical imaging systems. First, the Monte Carlo program was validated against previous measurements and simulations. Once validated, the Monte Carlo software modeled a digital mammography system by tracking photons through a voxelized software breast phantom, containing anatomical structures and breast masses, and following photons until they were absorbed by a selenium-based flat-panel detector. Simulations were performed for two compression conditions (standard compression and 12.5% reduced compression) and three photon flux conditions (constant flux, constant detector signal, and constant glandular dose). The results showed that reduced compression led to higher scatter fractions, as expected. For the constant photon flux condition, decreased compression also reduced glandular dose. For constant glandular dose, the SdNR for a 4 cm breast was 0.60 +/- 0.11 and 0.62 +/- 0.11 under standard and reduced compressions, respectively. For the 6 cm case with constant glandular dose, the SdNR was 0.50 +/- 0.11 and 0.49 +/- 0.10 under standard and reduced compressions, respectively. The results suggest that if a particular imaging system can handle an approximately 10% increase in total tube output and 10% decrease in detector signal, breast compression can be reduced by about 12% in terms of breast thickness with little impact on image quality or dose.
Saunders
Robert S
RS
Duke Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27705, USA. rssaun@gmail.com
Samei
Ehsan
E
D001940:Breast / Q000502:physiology*; D019245:Compressive Strength; D006801:Humans; D008327:Mammography / Q000379:methods*; D010173:Palpation / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity