TU-F-18C-02: Increasing Amorphous Selenium Thickness in Direct Conversion Flat-Panel Imagers for Contrast-Enhanced Dual-Energy Breast Imaging




Contrast-enhanced (CE) breast imaging using iodinated contrast agents requires imaging with x-ray spectra at energies greater than those used in mammography. Optimizing amorphous selenium (a-Se) flat panel imagers (FPI) for this higher energy range may increase lesion conspicuity.


We compare imaging performance of a conventional FPI with 200 μm a-Se conversion layer to a prototype FPI with 300 μm a-Se layer. Both detectors are evaluated in a Siemens MAMMOMAT Inspiration prototype digital breast tomosynthesis (DBT) system using low-energy (W/Rh 28 kVp) and high-energy (W/Cu 49 kVp) x-ray spectra. Detectability of iodinated lesions in dual-energy images is evaluated using an iodine contrast phantom. Effects of beam obliquity are investigated in projection and reconstructed images using different reconstruction methods. The ideal observer signal-to-noise ratio is used as a figure-of-merit to predict the optimal a-Se thickness for CE lesion detectability without compromising conventional full-field digital mammography (FFDM) and DBT performance.


Increasing a-Se thickness from 200 μm to 300 μm preserves imaging performance at typical mammographic energies (e.g. W/Rh 28 kVp), and improves the detective quantum efficiency (DQE) for high energy (W/Cu 49 kVp) by 30%. While the more penetrating high-energy x-ray photons increase geometric blur due to beam obliquity in the FPI with thicker a-Se layer, the effect on lesion detectability in FBP reconstructions is negligible due to the reconstruction filters employed. Ideal observer SNR for CE objects shows improvements in in-plane detectability with increasing a-Se thicknesses, though small lesion detectability begins to degrade in oblique projections for a-Se thickness above 500 μm.


Increasing a-Se thickness in direct conversion FPI from 200 μm to 300 μm improves lesion detectability in CE breast imaging with virtually no cost to conventional FFDM and DBT.

This work was partially supported by a research grant from Siemens Healthcare.