Mammography is used to screen asymptomatic women for breast cancer, and the typical breast cancer found using mammography is approximately 11 mm in diameter.  At this small size, removal of the lesion results in breast cancer cure in the majority of women.  However, there is a small class of women (~5% of all breast cancers) who are genetically predisposed to breast cancer (BRCA1 and BCRA2 genes), and in these women, more aggressive detection methods are needed.  In addition to BRCA1 and BRCA2 carriers who are at extraordinary risk of breast cancer, women with extremely dense breasts are at higher risk from breast cancer (by virtue of their dense breasts, with odds ratio from 4 to 6), and mammography is less sensitive in these women.  For women in these high-risk categories, most of whom have dense breasts that are poorly imaged by mammography, an imaging modality with better lesion detectabilty performance (contrast resolution) is needed.  While ultrasound, MR, and scinti-mammography techniques are being developed for breast cancer imaging, these modalities rely on contrast mechanisms that are less reliable than x-ray contrast – that is why they are not used for screening.  However, computed tomography (CT) does depend upon x-ray contrast mechanisms, but has about 10 times the contrast resolution as projection mammography.  CT is very capable of identifying soft tissue lesions in the 3-5 mm range – Such lesions are 10 to 50 times smaller in volume than the average 11 mm lesion found by mammography.  Therefore, CT has great potential for much earlier detection of breast cancer than mammography for high-risk patients.  In this feasibility study, we propose to thoroughly investigate the potential of dedicated breast CT using computer simulation techniques coupled with CT of cadaver breasts and mastectomy specimens.  Monte Carlo studies will be used to fully evaluate the glandular dose of breast CT, and imaging studies will be used to define the requirements of optimal CT acquisition.  Using CT scans of breast lesions from about ~10 mastectomy specimens, a breast tumor model will be developed.  The tumor model will be used with a series of ~20 cadaver breast CT data sets to conduct extensive ROC studies.  Computer observers will be used to define the Az versus tumor diameter curves for both CT and mammography.  Human observers will be used to validate and calibrate the more extensive computer observer results.  The results of this investigation should provide a clear understanding of the potential of breast CT as a tool to reduce breast cancer mortality in the population of women with dire risk of breast cancer.