The radiation dose involved in any medical imaging modality that uses ionizing radiation needs to be well understood by the medical physics and clinical community. This is especially true of screening modalities. Digital breast tomosynthesis (DBT) has recently been introduced into the clinic and is being used for screening for breast cancer in the general population. Therefore, it is important that the medical physics community have the required information to be able to understand, estimate, and communicate the radiation dose levels involved in breast tomosynthesis imaging. For this purpose, the American Association of Physicists in Medicine Task Group 223 on Dosimetry in Tomosynthesis Imaging has prepared this report that discusses dosimetry in breast imaging in general, and describes a methodology and provides the data necessary to estimate mean breast glandular dose from a tomosynthesis acquisition. In an effort to maximize familiarity with the procedures and data provided in this Report, the methodology to perform the dose estimation in DBT is based as much as possible on that used in mammography dose estimation.

Knowledge of x-ray attenuation is essential for developing and evaluating x-ray imaging technologies. For instance, techniques to better characterize cysts at mammography screening would be highly desirable to reduce recalls, but the development is hampered by the lack of attenuation data for cysts. We have developed a method to measure x-ray attenuation of tissue samples using a prototype photon-counting spectral mammography unit. The method was applied to measure the attenuation of 50 samples of breast cyst fluid and 50 samples of water. Spectral (energy-resolved) images of the samples were acquired and the image signal was mapped to equivalent thicknesses of two known reference materials, which can be used to derive the x-ray attenuation as a function of energy. The attenuation of cyst fluid was found to be significantly different from water. There was a relatively large natural spread between different samples of cyst fluid, whereas the homogeneity of each individual sample was found to be good; the variation within samples did not reach above the quantum noise floor. The spectral method proved stable between several measurements on the same sample. Further, chemical analysis and elemental attenuation calculation were used to validate the spectral measurement on a subset of the samples. The two methods agreed within the precision of the elemental attenuation calculation over the mammographic energy range.

Purpose: To evaluate a method for measuring breast density using photon-counting spectral mammography. Breast density is an indicator of breast cancer risk and diagnostic accuracy in mammography, and can be used as input to personalized screening, treatment monitoring and dose estimation. Methods: The measurement method employs the spectral difference in x-ray attenuation between adipose and fibro-glandular tissue, and does not rely on any a priori information. The method was evaluated using phantom measurements on tissue-equivalent material (slabs and breast-shaped phantoms) and using clinical data from a screening population (n ¼ 1329). A state-of-the-art nonspectral method for breast-density assessment was used for benchmarking. Results: The precision of the spectral method was estimated to be 1.5-1.8 percentage points (pp) breast density. Expected correlations were observed in the screening population for thickness versus breast density, dense volume, breast volume, and compression height. Densities ranged between 4.5% and 99.6%, and exhibited a skewed distribution with a mode of 12.5%, a median of 18.3%, and a mean of 23.7%. The precision of the nonspectral method was estimated to be 2.7-2.8 pp. The major uncertainty of the nonspectral method originated from the thickness estimate, and in particular thin/dense breasts posed problems compared to the spectral method. Conclusions: The spectral method yielded reasonable results in a screening population with a precision approximately two times that of the nonspectral method, which may improve or enable applications of breast-density measurement on an individual basis such as treatment monitoring and personalized screening.

Visualizing deep inside the tissue of a thick biological sample often poses severe constraints on image conditions. Standard restoration techniques (denoising and deconvolution) can then be very useful, allowing one to increase the signal-to-noise ratio and the resolution of the images. In this paper, we consider the problem of obtaining a good determination of the point-spread function (PSF) of a confocal microscope, a prerequisite for applying deconvolution to three-dimensional image stacks acquired with this system. Because of scattering and optical distortion induced by the sample, the PSF has to be acquired anew for each experiment. To tackle this problem, we used a screening approach to estimate the PSF adaptively and automatically from the images. Small PSF-like structures were detected in the images, and a theoretical PSF model reshaped to match the geometric characteristics of these structures. We used numerical experiments to quantify the sensitivity of our detection method, and we demonstrated its usefulness by deconvolving images of the hearing organ acquired in vitro and in vivo.