Computational Modeling of Ultrasound C-Scan Imaging Using Transmitted Signal Peak Density

Abstract

Ultrasound measurement is a relatively inexpensive and commonly used imaging tool in the health sector. The through-transmission process of ultrasound measurement has been extensively evaluated for detecting abnormalities in tissue pathology. Compared to standard imaging parameters such as amplitude and time of flight, quantitative ultrasound parameters in the frequency domain can provide additional details regarding tissue microstructures. In this study, pressure magnitude or amplitude variation in the frequency spectrum of the received signal was evaluated as a potential imaging technique using the spectral peak density parameter. Computational C-scan imaging analysis was developed through a finite element model. The magnitude variation in the received signal showed different patterns while interacting with and without inclusions. Images were reconstructed based on peak density values that varied with the presence of solid structure. The computational results were verified with the experimental C-scan imaging results from the literature. It was found that magnitude variation can be an effective parameter for C-scan imaging of thin structures. The feasibility of the study was further extended to identify the structure’s relative position along with the sample depth during C-scan imaging. While moving the structure in the direction of the sample depth, the pressure magnitude variation strongly followed a second-degree polynomial trend.