The aim of this work is to provide a simulation framework for generation of synthetic tomosynthesis images to
be used for evaluation of future developments in the field of tomosynthesis. An anthropomorphic software tissue
phantom was previously used in a number of applications for evaluation of acquisition modalities and image
post-processing algorithms for mammograms. This software phantom has been extended for similar use with
tomosynthesis. The new features of the simulation framework include a finite element deformation model to
obtain realistic mammographic deformation and projection simulation for a variety of tomosynthesis geometries.
The resulting projections are provided in DICOM format to be applicable for clinically applied reconstruction
algorithms. Examples of simulations using parameters of a currently applied clinical setup are presented. The
overall simulation model is generic, allowing multiple degrees of freedom to cover anatomical variety in the amount
of glandular tissue, degrees of compression, material models for breast tissues, and tomosynthesis geometries.
Software breast phantoms offer greater flexibility in generating synthetic breast images compared to physical phantoms.
The realism of such generated synthetic images depends on the method for simulating the three-dimensional breast
anatomical structures. We present here a novel algorithm for computer simulation of breast anatomy. The algorithm
simulates the skin, regions of predominantly adipose tissue and fibro-glandular tissue, and the matrix of adipose tissue
compartments and Cooper's ligaments. The simulation approach is based upon a region growing procedure; adipose
compartments are grown from a selected set of seed points with different orientation and growth rate. The simulated
adipose compartments vary in shape and size similarly to the anatomical breast variation, resulting in much improved
phantom realism compared to our previous simulation based on geometric primitives. The proposed simulation also has
an improved control over the breast size and glandularity. Our software breast phantom has been used in a number of
applications, including breast tomosynthesis and texture analysis optimization.
Women with dense breasts have an increased risk of breast cancer. Breast density is typically measured as the percent
density (PD), the percentage of non-fatty (i.e., dense) tissue in breast images. Mammographic PD estimates vary, in
part, due to the projective nature of mammograms. Digital breast tomosynthesis (DBT) is a novel radiographic method
in which 3D images of the breast are reconstructed from a small number of projection (source) images, acquired at
different positions of the x-ray focus. DBT provides superior visualization of breast tissue and has improved sensitivity
and specificity as compared to mammography. Our long-term goal is to test the hypothesis that PD obtained from DBT
is superior in estimating cancer risk compared with other modalities. As a first step, we have analyzed the PD estimates
from DBT source projections since the results would be independent of the reconstruction method. We estimated PD
from MLO mammograms (PD<sub>M</sub>) and from individual DBT projections (PD<sub>T</sub>). We observed good agreement between
PD<sub>M</sub> and PD<sub>T</sub> from the central projection images of 40 women. This suggests that variations in breast positioning, dose,
and scatter between mammography and DBT do not negatively affect PD estimation. The PD<sub>T</sub> estimated from
individual DBT projections of nine women varied with the angle between the projections. This variation is caused by
the 3D arrangement of the breast dense tissue and the acquisition geometry.