Based on the results obtained from the four groups of women in our study, the "50-50" breast is not a representative model of the breast composition.

A dedicated extremity cone-beam CT scanner capable of imaging upper and lower extremities (including weight-bearing examinations) provides sufficient image quality and favorable dose characteristics to warrant further evaluation for clinical use.

Breast metrics were examined and a number of parameters were defined which may be useful for breast modeling. The reported data may provide researchers with useful information for characterizing the breast for various imaging or dosimetry tasks.

The effect of breast skin thickness on dosimetry in mammography was investigated. Breast computed tomography ͑CT͒ acquisition techniques, combined with algorithms designed for determining specific breast metrics, were useful for estimating skin thickness. A radial-geometry edge detection scheme was implemented on coronal reconstructed breast CT ͑bCT͒ images to measure the breast skin thickness. Skin thickness of bilateral bCT volume data from 49 women and unilateral bCT volume data from 2 women ͑10 healthy women and 41 women with BIRADS 4 and 5 diagnoses͒ was robustly measured with the edge detection scheme. The mean breast skin thickness ͑Ϯinter-breast standard deviation͒ was found to be 1.45Ϯ 0.30 mm. Since most current published normalized glandular dose ͑D gN ͒ coefficients are based on the assumption of a 4-mm breast skin thickness, the D gN values computed with Monte Carlo techniques will increase up to 18% due to the thinner skin layers ͑e.g., 6-cm 50% glandular breast, 28 kVp Mo-Mo spectrum͒. The thinner skin dimensions found in this study suggest that the current D gN values used for mammographic dosimetry lead to a slight underestimate in glandular dose.

Previous work ͓Burgess et al., Med. Phys. 28, 419-437 ͑2001͔͒ has shown that anatomical noise in projection mammography results in a power spectrum well modeled over a range of frequencies by a power law, and the exponent ͑␤͒ of this power law plays a critical role in determining the size at which a growing lesion reaches the threshold for detection. In this study, the authors evaluated the power-law model for breast computed tomography ͑bCT͒ images, which can be thought of as thin sections through a three-dimensional ͑3D͒ volume. Under the assumption of a 3D power law describing the distribution of attenuation coefficients in the breast parenchyma, the authors derived the relationship between the power-law exponents of bCT and projection images and found it to be ␤ section = ␤ proj − 1. They evaluated this relationship on clinical images by comparing bCT images from a set of 43 patients to Burgess' findings in mammography. They were able to make a direct comparison for 6 of these patients who had both a bCT exam and a digitized film-screen mammogram. They also evaluated segmented bCT images to investigate the extent to which the bCT power-law exponent can be explained by a binary model of attenuation coefficients based on the different attenuation of glandular and adipose tissue. The power-law model was found to be a good fit for bCT data over frequencies from 0.07 to 0.45 cyc/ mm, where anatomical variability dominates the spectrum. The average exponent for bCT images was 1.86. This value is close to the theoretical prediction using Burgess' published data for projection mammography and for the limited set of mammography data available from the authors' patient sample. Exponents from the segmented bCT images ͑average value: 2.06͒ were systematically slightly higher than bCT images, with substantial correlation between the two ͑r = 0.84͒.