Evaluation of statistical breast phantoms with higher resolution
In previous work, we generated computational breast phantoms by using a principal component analysis (PCA) or "Eigenbreast" technique. For this study, we sought to address resolution limitations in the previous synthesized breast phantoms by analyzing new human subject data set with higher resolution. We utilize PCA to sample input breast cases, then by using weighted sums along the different eigenvectors or "eigenbreasts," a number of new cases can be generated. While breasts can vary in structure and form, we used a series of compressed breasts derived from human subject breast CT volumes to create the eigenbreasts. We used an initial set of thirty-five phantoms from a new CT patient population with 155x155x155 μm3 voxel size. The training set and synthetized phantoms were evaluated by power law exponent β and changes in volumetric breast density as a result of the PCA process. The synthetic phantoms were found to have similar β and fibroglandular density distributions to the training dataset. Individual synthetic phantoms appeared to capture glandular features present in the training phantoms but had visually different texture features. This work shows that earlier work on the eigenbreast technique can be extended to newer datasets with higher resolution and produce synthetic phantoms that retain the quantitative properties of training data.
