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[Preprint]. 2024 Feb 1:arXiv:2402.00812v1.

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

Amar Kavuri  1 , Mini Das  1   2
Affiliations

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

Amar Kavuri et al. ArXiv. .

Update in

Abstract

Purpose: Digital phantoms are one of the key components of virtual imaging trials (VITs) that aims 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° and 6° in Bakic and XCAT breast phantoms respectively for 3-mm lesion detection task 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.

Conclusion: Our results point to the critical need to evaluate realism in digital phantoms as well as ensuring sufficient structural variations at spatial frequencies relevant to the signal size for an intended task. In addition, standardizing phantom generation and validation tools might aid in lower discrepancies among independently conducted VITs for system or algorithmic optimizations.

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Figures

FIG. 1.
FIG. 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.
FIG. 2.
FIG. 2.
Example gaze pattern with fixations and saccades on DBT slices of 25% dense (top) and 50% dense (bottom) phantoms.
FIG. 3.
FIG. 3.
A sample lesion present regions of DBT slices of both phantoms acquired with different number of projections.
FIG. 4.
FIG. 4.
Human observer performance plotted as localization ROC (LROC) curves for a sample acquisition of 35 projections over 60° in 3-mm mass detection study in Bakic phantom (top) and XCAT breast phantom (bottom) backgrounds.
FIG. 5.
FIG. 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.
FIG. 6.
FIG. 6.
The power spectra analysis of DBT slices of both type of backgrounds for a sample acquisition protocol of 60° arc span, 35 projections suggest that lack of small and sharp structures in XCAT breast phantoms resulted in lower spectral density at higher frequencies than that of Bakic phantoms.
FIG. 7.
FIG. 7.
The average amount of time spent and the average number of fixations made on images (includes lesion absent and lesion present images) plotted for both phantom types. Observers spent longer time and made more fixations to make decisions on images with Bakic phantom backgrounds in comparison to those with XCAT breast backgrounds.
FIG. 8.
FIG. 8.
Average value of first hit time, lesion dwell time and number of fixations on lesion were plotted against AUC value of each observer (total 6) for both phantom types and two densities.
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References

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