The importance of modeling normal mammographic structure in optimizing flat-panel CT breast imaging systems

摘要

In recent years, there has been interest in exploring the feasibility of CT breast imaging using flat-panel digital detectors in a truncated cone-beam geometry. Preliminary results are promising and it appears as if 3D tomographic imaging of the breast has great potential for reducing the masking effect of superimposed parenchymal structure typically observed with conventional mammography. In this study, a mathematical framework used for determining optimal design and acquisition parameters for such a CT breast imaging system is described. The ideal observer SNR is used as a figure-of-merit, under the assumptions that the imaging system is linear and shift-invariant. Computation of the ideal observer SNR used a parallel-cascade model to predict signal and noise propagation through the detector, as well as a realistic model of the lesion detection task in breast imaging. For all optimizations discussed here, the total mean glandular dose for a CT breast imaging study is constrained to be approximately equivalent to that of a two-view conventional mammography study. The framework presented is used to explore the affect of the specific task on the optimal exposure technique of flat-panel CT breast imaging. In particular, it is observed that modeling the normal mammographic structure in the projection images can sometimes impact the optimal kVp settings.