Ultrasound is commonly used as an imaging tool in the medical sector. Compared to standard ultrasound imaging, quantitative ultrasound analysis can provide more details about a material microstructure. In this study, quantitative ultrasound analysis was conducted through computational modeling to detect various breast duct pathologies in the surgical margin tissue. Both pulse-echo and pitch-catch methods were evaluated for a high-frequency (22-41 MHz) ultrasound analysis. The computational surgical margin modeling was based on various conditions of breast ducts, such as normal duct, ductal hyperplasia, DCIS, and calcification. In each model, ultrasound pressure magnitude variation in the frequency spectrum was analyzed through peak density and mean-peak-to-valley distance (MPVD) values. Furthermore, the spectral patterns of all the margin models were compared to extract more pathology-based information. For the pitch-catch mode, only peak density provided a trend in relation to different duct pathologies. For the pulse-echo mode, only the MPVD was able to do that. From the spectral comparison, it was found that overall pressure magnitude, spectral variation, peak pressure magnitude, and corresponding frequency level provided helpful information to differentiate various pathologies in the surgical margin.
Keywords: breast cancer; ductal carcinoma in situ; finite element analysis; peak density; pitch-catch; pulse-echo; quantitative ultrasound; surgical margin.