Examining the Influence of Digital Phantom Models in Virtual Imaging Trials for Tomographic Breast Imaging

TL;DR

This study addresses how differences in digital breast phantoms affect virtual imaging trial outcomes for tomographic breast imaging. By comparing Bakic and XCAT phantoms across density levels, it demonstrates phantom-induced shifts in optimal acquisition parameters (e.g., arc sampling) and reveals that higher high-frequency content in Bakic phantoms increases the need for more projections, while XCAT backgrounds show lower high-frequency content and different optimization trends. The work also links observer gaze metrics to diagnostic performance, suggesting gaze data can reflect task difficulty and interpretation processes in VITs. Overall, the findings underscore the need for standardizing phantom frequency content and structure when comparing VIT results across studies and modalities, and they highlight gaze analysis as a valuable tool for understanding task difficulty in simulated imaging trials.

Abstract

Purpose: Digital phantoms are one of the key components of virtual imaging trials (VITs) that aim to assess and optimize new medical imaging systems and algorithms. However, these phantoms vary in their voxel resolution, appearance, and structural details. This study aims to examine whether and how variations between digital phantoms influence system optimization with digital breast tomosynthesis (DBT) as a chosen modality. Methods: We selected widely used and open-access digital breast phantoms generated with different methods. For each phantom type, we created an ensemble of DBT images to test acquisition strategies. Human observer localization ROC (LROC) was used to assess observer performance studies for each case. Noise power spectrum (NPS) was estimated to compare the phantom structural components. Further, we computed several gaze metrics to quantify the gaze pattern when viewing images generated from different phantom types. Results: Our LROC results show that the arc samplings for peak performance were approximately 2.5 degrees and 6 degrees in Bakic and XCAT breast phantoms respectively for 3-mm lesion detection tasks and indicate that system optimization outcomes from VITs can vary with phantom types and structural frequency components. Additionally, a significant correlation (p= 0.01) between gaze metrics and diagnostic performance suggests that gaze analysis can be used to understand and evaluate task difficulty in VITs.

Figures (8)

• Figure 1: A sample slice of 25% VGF Bakic (top left) and XCAT breast (top right) phantoms and the corresponding 1-mm DBT slices with 3-mm spherical lesion on the bottom.
• Figure 2: Example gaze pattern with fixations and saccades on DBT slices of 25% dense (top) and 50% dense (bottom) phantoms.
• Figure 3: A sample lesion present regions of DBT slices of both phantoms acquired with different number of projections.
• Figure 4: Human observer performance plotted as localization ROC (LROC) curves for a sample acquisition of 35 projections over $60^\circ$ in 3-mm mass detection study in Bakic phantom (top) and XCAT breast phantom (bottom) backgrounds.
• Figure 5: Human observer performance plotted as area under LROC (AUC) against number of projections in 3-mm mass detection study in Bakic phantom (top) and XCAT breast phantom (bottom) backgrounds. The results suggest the optimal configurations does not change with the task difficulty but changes with the background structure type.