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Soft-tissue lesion and microcalcification detectability in cone-beam breast CT: cascaded system analysis
Thomas Larsen1, Hsin Wu Tseng2, Jing-Tzyh Alan Chiang2
1University of Arizona, Department of Biomedical Engineering, Tucson, Arizona, United States.
Journal of Medical Imaging (Bellingham, Wash.)
|December 17, 2025
Summary
Dedicated breast computed tomography (CT) system optimization should prioritize detecting microcalcification clusters over soft-tissue lesions. While soft-tissue lesion detection generally shows higher detectability, optimizing for microcalcifications ensures better performance across various screening conditions.
Area of Science:
- Medical Imaging Physics
- Radiological Sciences
- Biomedical Engineering
Background:
- Dedicated breast computed tomography (CT) offers advanced imaging capabilities for breast cancer detection.
- System optimization is crucial for maximizing the performance of breast CT in identifying various lesion types.
- Understanding the trade-offs between detecting soft-tissue lesions and microcalcifications is essential for effective system design.
Purpose of the Study:
- To evaluate the performance of dedicated breast CT for detecting soft-tissue lesions.
- To compare this performance with the detection of microcalcification clusters.
- To determine which lesion type is more appropriate for optimizing breast CT system parameters.
Main Methods:
- A cascaded systems analysis was employed, modeling signal and noise propagation through the imaging chain.
- Two lesion types were simulated: a 4 mm soft-tissue mass and a cluster of 220 μm microcalcifications.
- Detectability indices were calculated using three numerical observer models under various acquisition conditions and a fixed mean glandular dose of 4.5 mGy.
Main Results:
- Detectability index trends for soft-tissue lesions and microcalcification clusters showed inverse relationships with varying X-ray tube voltages and filtrations.
- Across 150 different parameter combinations (kV settings, filter thicknesses, scintillator thicknesses), the detectability index for soft-tissue lesions consistently surpassed that for microcalcification clusters for all numerical observer models.
- This suggests a potential need for compromise in system optimization to balance the detection of both lesion types.
Conclusions:
- For unknown lesion types, as in breast cancer screening, optimizing dedicated breast CT systems for microcalcification cluster detection is recommended.
- This approach ensures a higher detectability index for soft-tissue lesions compared to microcalcifications under all investigated conditions.
- Prioritizing microcalcification detection in system optimization provides a more robust performance for general breast cancer screening scenarios.
