Room: Track 1
Purpose: Automatic exposure control (AEC) systems are important components of digital mammography systems that maintain image quality at an appropriate dose. The 2018 ACR QC manual includes an evaluation of AEC performance using of one of several recommend tissue-equivalent attenuators and measurement of the signal-to-noise ratio (SNR). We investigated how the choice of attenuator affects the measured AEC system performance.
Methods: We evaluated AEC performance of four mammography systems with seven sets of tissue-equivalent attenuators at two attenuator thicknesses. All four systems used the 2D mode, and one also used DBT. We imaged each attenuator material and thickness combination with an AEC mode that automatically selects the target/filter, tube potential, and current-time. We used software to automatically place a 1 cm2 ROI on each “For Processing” image centered laterally 3 cm from the chest-wall edge. We calculated the differences in signal, noise, and SNR normalized to the mean value for each system across all attenuators.
Results: Identical sets of attenuators yielded high reproducibility with similar signal, noise, and SNR values (average differences of 1.1%, 3.9%, and 3.3%). In 2D mode with 4.0 cm of attenuator, the mean background signal did not vary when different attenuators were used (average difference of 10%). However, the noise and SNR exhibited large differences (52% and 49%). Similar trends were observed with 8.0 cm of attenuator (average differences in signal, noise, and SNR of 10%, 46%, and 43%) and in DBT mode (average differences in signal, noise, and SNR of 4%, 16%, and 18%).
Conclusions: The tissue-equivalent attenuator can impact the measured AEC system performance, with differences in SNR over 50%, likely due to the structural noise characteristics of the attenuators. The choice of attenuator material will affect measured AEC system performance when evaluation is based on longitudinal changes in SNR or absolute SNR values.
IM- Breast X-Ray Imaging: Quality Control and Image Quality Assessment