Model of malignant breast biopsies and predictions of their WAXS energy integrated signals

R. J. LeClair; M. Brunet

doi:10.1117/12.2508379

1 March 2019 Model of malignant breast biopsies and predictions of their WAXS energy integrated signals

R. J. LeClair, M. Brunet

Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 1094803 (2019) https://doi.org/10.1117/12.2508379
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

A numerical study of the wide angle x-ray scatter (WAXS) energy integrated signals (EISs) from breast biopsies was conducted. A benign biopsy was chosen as fibroglandular (fib) tissue whereas biopsies with cancer were approximated as consisting of fib tissue and a cluster of epithelial cells. The grouping of cells represented the malignant portion. The EISs due to scatter were computed for biopsies of thickness d_bio = 2 mm, 5 mm, 10 mm and 20 mm. For the malignant biopsies, the fractional volumes of cells (ν_cell) studied ranged from 0.01 to 0.1. Incident 2 mm diam beams of 30 kV, 50 kV, 80 kV, and 140 kV were considered. The tube current and exposure time were 3 mA and 1 minute, respectively. The WAXS signals were computed by adding the signals from annular detectors subtending scattering angles θ = 2°, 3°, , 23° and solid angles Ωθ = 2.0 x π x [cos(θ - ∆θ/2) - cos(θ + ∆θ/2)] x cos θ where ∆θ = 1°. Let the EIS due to a malignant biopsy be EIS^m_s and that of a benign one, EIS^b_s. With these signals, values of SNR were computed. The 30 kV beam provided SNRs < 5 for the lowest entrance exposure, X = 0.0015 C/kg. For biopsies with d_bio = 2, 5, 10, 20 mm and ν_cell = 0.1, the SNRs were 28.0, 38.2, 43.0, and 40.0. The findings suggest that there is potential to use WAXS EISs to diagnose malignancy in breast biopsies.

Conference Presentation

Citation Download Citation

R. J. LeClair and M. Brunet "Model of malignant breast biopsies and predictions of their WAXS energy integrated signals ", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094803 (1 March 2019); https://doi.org/10.1117/12.2508379

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