Impact of super-resolution and image acquisition on the detection of calcifications in digital breast tomosynthesis

A virtual clinical trial (VCT) method is proposed to determine the limit of calcification detection in tomosynthesis.

Breast anatomy, focal findings, image acquisition, and interpretation (n = 14 readers) were simulated using screening data (n = 660 patients). Calcifications (0.2–0.4 mm³) were inserted into virtual breast phantoms. Digital breast tomosynthesis (DBT) acquisitions were simulated assuming various acquisition geometries: source motion (continuous and step-and-shoot), detector element size (140 and 70 µm), and reconstructed voxel size (35–140 µm). VCT results were estimated using multiple-reader multiple-case analyses and d’ statistics. Signal-to-noise (SNR) analyses were also performed using BR3D phantoms.

Source motion and reconstructed voxel size demonstrated significant changes in the performance of imaging systems. Acquisition geometries that use 70 µm reconstruction voxel size and step-and-shoot motion significantly improved calcification detection. Comparing 70 with 100 µm reconstructed voxel size for step-and-shoot, the ΔAUC was 0.0558 (0.0647) and d’ ratio was 1.27 (1.29) for 140 µm (70 µm) detector element size. Comparing step-and-shoot with a continuous motion for a 70 µm reconstructed voxel size, the ΔAUC was 0.0863 (0.0434) and the d’ ratio was 1.40 (1.19) for 140 µm (70 µm) detector element. Small detector element sizes (e.g., 70 µm) did not significantly improve detection. The SNR results with the BR3D phantom show that calcification detection is dependent upon reconstructed voxel size and detector element size, supporting VCT results with comparable agreement (ratios: d’ = 1.16 ± 0.11, SNR = 1.34 ± 0.13).

DBT acquisition geometries that use super-resolution (smaller reconstructed voxels than the detector element size) combined with step-and-shoot motion have the potential to improve the detection of calcifications.

Calcifications may not always be discernable in tomosynthesis because of differences in acquisition and reconstruction methods. VCTs can identify strategies to optimize acquisition and reconstruction parameters for calcification detection in tomosynthesis, most notably through super-resolution in the reconstruction.